{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Cost_Sensitive_Learning)=\n",
    "# Cost-sensitive learning\n",
    "\n",
    "\n",
    "Cost-sensitive learning is a subfield of machine learning that addresses classification problems where the misclassification costs are not equal {cite}`fernandez2018learning,elkan2001foundations,ling2008cost`. Cost-sensitive problems occur in many disciplines such as medicine (e.g., disease detection), engineering (e.g., machine failure detection), transport (e.g., traffic-jam detection), finance (e.g., fraud detection), and so forth. They are often related to the class-imbalance problem since in most of these problems, the goal is to detect events that are rare. The training datasets therefore typically contain fewer examples of the event of interest.  \n",
    "\n",
    "We already addressed fraud detection as a cost-sensitive problem in [Chapter 4, Cost Matrix](Performance_CostMatrix). The section pointed out the cost matrix as the standard way to quantify the misclassification costs. Denoting by $C$ the *cost matrix*, its entries $c(i,j)$ quantify the cost of predicting class $i$ when the true class is $j$ {cite}`elkan2001foundations`. For a binary classification problem, the cost matrix is a $2*2$ matrix, as illustrated in Fig. 1. \n",
    "\n",
    "![](images/cost_matrix.png)\n",
    "<div align=\"center\">Fig. 1. Example of cost matrix.</div>    \n",
    "\n",
    "Correct classifications have a cost of zero, that is, $c_{00}=c_{11}=0$. Misclassification costs are however in practice difficult to estimate. As discussed in [Chapter 4, Cost Matrix](Performance_CostMatrix), missing a fraudulent transaction (false negative) involves a loss directly related to the amount of the transaction, but also on further fraudulent uses of the card, and on the company reputation. At the same time, the blocking of transactions that are legitimate (false positive) causes inconvenience to customers, generates useless investigation costs, and also impacts the company reputation.  \n",
    "\n",
    "In cost-sensitive imbalanced problems, the most popular heuristic approach to estimate the costs lies in utilizing the imbalance ratio (IR). Let us denote by $\\mathcal{X}$ the imbalanced dataset, with $\\mathcal{X}_0$ and $\\mathcal{X}_1$ being the subsets of samples belonging to the majority and minority class, respectively. The IR of the dataset $\\mathcal{X}$ is defined as {cite}`JMLR:v18:16-365`:\n",
    "\n",
    "$$\n",
    "IR=\\frac{|\\mathcal{X}_1|}{|\\mathcal{X}_0|}\n",
    "$$\n",
    "\n",
    "where $|·|$ denotes the cardinality of a set. In this setting, $C(i,j) = IR$ and $C(j,i) = 1$, where the minority class is the i-th class, and the majority class is the j-th class. It is worth noting that using the IR as the cost for the majority class balances the overall cost of the two classes, that is, $|\\mathcal{X}_1|=IR*|\\mathcal{X}_0|$. The resulting cost matrix for a 2-class problem is given in Fig. 2.\n",
    "\n",
    "![](images/cost_matrix_IR.png)\n",
    "<div align=\"center\">Fig. 2. Cost matrix for imbalanced data. The cost of a false negative is 1, and the cost of a false positive is the imbalance ratio (IR).</div>      \n",
    "\n",
    "Using the IR to set the misclassification costs is usually a good heuristic. It however has some limitations, in particular related to small sample size, class overlapping, and noisy or borderline instances {cite}`fernandez2018learning`. A common complementary practice consists in considering the misclassification costs as a hyperparameter to be identified through model selection. \n",
    "\n",
    "Python sklearn provides support for cost-sensitive learning for most baseline classifiers thanks to the `class_weight` parameter. The parameter allows to specify costs in three different ways:\n",
    "\n",
    "* `None`: The misclassification costs are set to 1 (default)\n",
    "* `balanced`: The costs are set according to the imbalance ratio (as in Fig. 2)\n",
    "* `{0:c10, 1:c01}`: The misclassification costs are explicitly set for the two classes by means of a dictionary.\n",
    "\n",
    "The use of class weights usually implies a modification in the loss function of the learning algorithm. The modification depends on the type of algorithm. By strongly penalizing mistakes on the minority class, cost-sensitive learning improves their importance during the classifier training step. This pushes the decision boundary away from these instances, allowing to improve generalization on the minority class {cite}`fernandez2018learning,gupta2020class`.\n",
    "\n",
    "This section presents how cost-sensitive learning can be used with the Python sklearn library. For better visualization, we first rely on a simple imbalanced dataset with two variables to illustrate how different misclassification costs change the decision boundaries. We then apply the approach to the larger simulated dataset of transaction data.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 63060  100 63060    0     0   221k      0 --:--:-- --:--:-- --:--:--  220k\n"
     ]
    }
   ],
   "source": [
    "# Initialization: Load shared functions and simulated data \n",
    "\n",
    "# Load shared functions\n",
    "!curl -O https://raw.githubusercontent.com/Fraud-Detection-Handbook/fraud-detection-handbook/main/Chapter_References/shared_functions.py\n",
    "%run shared_functions.py\n",
    "#%run ../Chapter_References/shared_functions.ipynb\n",
    "\n",
    "# Get simulated data from Github repository\n",
    "if not os.path.exists(\"simulated-data-transformed\"):\n",
    "    !git clone https://github.com/Fraud-Detection-Handbook/simulated-data-transformed\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Imbalanced_Learning_Illustrative_Example)=\n",
    "## Illustrative example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For illustrative purposes, let us first consider a simple classification task. We use the `make_classification` function of the sklearn library to generate a two-class imbalanced dataset with 5000 examples. The dataset contains 95% of examples of class 0 and 5% of examples of class 1.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = sklearn.datasets.make_classification(n_samples=5000, n_features=2, n_informative=2,\n",
    "                                            n_redundant=0, n_repeated=0, n_classes=2,\n",
    "                                            n_clusters_per_class=1,\n",
    "                                            weights=[0.95, 0.05],\n",
    "                                            class_sep=0.5, random_state=0)\n",
    "\n",
    "dataset_df = pd.DataFrame({'X1':X[:,0],'X2':X[:,1], 'Y':y})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the two classes slighly overlap, as illustrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "fig_distribution, ax = plt.subplots(1, 1, figsize=(5,5))\n",
    "\n",
    "groups = dataset_df.groupby('Y')\n",
    "for name, group in groups:\n",
    "    ax.scatter(group.X1, group.X2, edgecolors='k', label=name,alpha=1,marker='o')\n",
    "    \n",
    "ax.legend(loc='upper left', \n",
    "          bbox_to_anchor=(1.05, 1),\n",
    "          title=\"Class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now train a decision tree to separate the two classes. We use a decision tree of depth 5, and a stratified 5-fold cross-validation to assess the performances of the classifier. The performances are assessed in terms of AUC ROC, Average precision, and balanced accuracy. The class weights are set to 1 for both classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5,class_weight={0:1,1:1},random_state=0)\n",
    "cv = sklearn.model_selection.StratifiedKFold(n_splits=5, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results_ = sklearn.model_selection.cross_validate(classifier, X, y, cv=cv,\n",
    "                                                     scoring=['roc_auc',\n",
    "                                                              'average_precision',\n",
    "                                                              'balanced_accuracy'],\n",
    "                                                     return_estimator=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performances for each fold are returned in the `cv_results_` dictionary, which is better visualized as a DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>estimator</th>\n",
       "      <th>test_roc_auc</th>\n",
       "      <th>test_average_precision</th>\n",
       "      <th>test_balanced_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.006</td>\n",
       "      <td>0.004</td>\n",
       "      <td>DecisionTreeClassifier(class_weight={0: 1, 1: ...</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.006</td>\n",
       "      <td>0.004</td>\n",
       "      <td>DecisionTreeClassifier(class_weight={0: 1, 1: ...</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006</td>\n",
       "      <td>0.011</td>\n",
       "      <td>DecisionTreeClassifier(class_weight={0: 1, 1: ...</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.005</td>\n",
       "      <td>DecisionTreeClassifier(class_weight={0: 1, 1: ...</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.007</td>\n",
       "      <td>0.004</td>\n",
       "      <td>DecisionTreeClassifier(class_weight={0: 1, 1: ...</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.447</td>\n",
       "      <td>0.738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time                                          estimator  \\\n",
       "0     0.006       0.004  DecisionTreeClassifier(class_weight={0: 1, 1: ...   \n",
       "1     0.006       0.004  DecisionTreeClassifier(class_weight={0: 1, 1: ...   \n",
       "2     0.006       0.011  DecisionTreeClassifier(class_weight={0: 1, 1: ...   \n",
       "3     0.009       0.005  DecisionTreeClassifier(class_weight={0: 1, 1: ...   \n",
       "4     0.007       0.004  DecisionTreeClassifier(class_weight={0: 1, 1: ...   \n",
       "\n",
       "   test_roc_auc  test_average_precision  test_balanced_accuracy  \n",
       "0         0.933                   0.616                   0.842  \n",
       "1         0.871                   0.493                   0.739  \n",
       "2         0.897                   0.493                   0.799  \n",
       "3         0.928                   0.591                   0.812  \n",
       "4         0.901                   0.447                   0.738  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = round(pd.DataFrame(cv_results_),3)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us take the mean and standard deviation of the performances across all folds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007+/-0.001</td>\n",
       "      <td>0.006+/-0.003</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fit time (s) Score time (s)        AUC ROC Average Precision  \\\n",
       "0  0.007+/-0.001  0.006+/-0.003  0.906+/-0.025     0.528+/-0.072   \n",
       "\n",
       "  Balanced accuracy  \n",
       "0     0.786+/-0.046  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_mean = list(results.mean().values)\n",
    "results_std = list(results.std().values)\n",
    "\n",
    "pd.DataFrame([[str(round(results_mean[i],3))+'+/-'+str(round(results_std[i],3)) for i in range(len(results))]],\n",
    "            columns=['Fit time (s)','Score time (s)','AUC ROC','Average Precision','Balanced accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performances are rather good since the AUC ROC is well beyond $0.5$ and the average precision over $0.05$. The balanced accuracy is however not so high, suggesting that the decision boundary misclassifies many of the samples from the minority class. \n",
    "\n",
    "Let us finally plot the decision boundary provided by one of the decision trees. We use the decision tree obtained from the first fold of the cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def plot_decision_boundary_classifier(ax, \n",
    "                                      classifier,\n",
    "                                      train_df,\n",
    "                                      input_features=['X1','X2'],\n",
    "                                      output_feature='Y',\n",
    "                                      title=\"\",\n",
    "                                      fs=14,\n",
    "                                      plot_training_data=True):\n",
    "\n",
    "    plot_colors = [\"tab:blue\",\"tab:orange\"]\n",
    "\n",
    "    x1_min, x1_max = train_df[input_features[0]].min() - 1, train_df[input_features[0]].max() + 1\n",
    "    x2_min, x2_max = train_df[input_features[1]].min() - 1, train_df[input_features[1]].max() + 1\n",
    "    \n",
    "    plot_step=0.1\n",
    "    xx, yy = np.meshgrid(np.arange(x1_min, x1_max, plot_step),\n",
    "                         np.arange(x2_min, x2_max, plot_step))\n",
    "\n",
    "    Z = classifier.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:,1]\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    ax.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu_r,alpha=0.3)\n",
    "\n",
    "    if plot_training_data:\n",
    "        # Plot the training points\n",
    "        groups = train_df.groupby(output_feature)\n",
    "        for name, group in groups:\n",
    "            ax.scatter(group[input_features[0]], group[input_features[1]], edgecolors='black', label=name)\n",
    "        \n",
    "    ax.set_title(title, fontsize=fs)\n",
    "    ax.set_xlabel(input_features[0], fontsize=fs)\n",
    "    ax.set_ylabel(input_features[1], fontsize=fs)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve the decision tree from the first fold of the cross-validation\n",
    "classifier_0 = cv_results_['estimator'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve the indices used for the training and testing of the first fold of the cross-validation\n",
    "(train_index, test_index) = next(cv.split(X, y))\n",
    "\n",
    "# Recreate the train and test DafaFrames from these indices\n",
    "train_df = pd.DataFrame({'X1':X[train_index,0], 'X2':X[train_index,1], 'Y':y[train_index]})\n",
    "test_df = pd.DataFrame({'X1':X[test_index,0], 'X2':X[test_index,1], 'Y':y[test_index]})\n",
    "input_features = ['X1','X2']\n",
    "output_feature = 'Y'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "fig_decision_boundary, ax = plt.subplots(1, 3, figsize=(5*3,5))\n",
    "\n",
    "plot_decision_boundary_classifier(ax[0], classifier_0,\n",
    "                                  train_df,\n",
    "                                  title=\"Decision surface of the decision tree\\n With training data\",\n",
    "                                  plot_training_data=True)\n",
    "\n",
    "plot_decision_boundary_classifier(ax[1], classifier_0,\n",
    "                                  train_df,\n",
    "                                  title=\"Decision surface of the decision tree\\n\",\n",
    "                                  plot_training_data=False)\n",
    "\n",
    "\n",
    "plot_decision_boundary_classifier(ax[2], classifier_0,\n",
    "                                  test_df,\n",
    "                                  title=\"Decision surface of the decision tree\\n With test data\",\n",
    "                                  plot_training_data=True)\n",
    "\n",
    "ax[-1].legend(loc='upper left', \n",
    "              bbox_to_anchor=(1.05, 1),\n",
    "              title=\"Class\")\n",
    "\n",
    "sm = plt.cm.ScalarMappable(cmap=plt.cm.RdYlBu_r, norm=plt.Normalize(vmin=0, vmax=1))\n",
    "cax = fig_decision_boundary.add_axes([0.93, 0.15, 0.02, 0.5])\n",
    "fig_decision_boundary.colorbar(sm, cax=cax, alpha=0.3, boundaries=np.linspace(0, 1, 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HXbp0UdqvQ4cOcHFxwZIlS/Dw4UM8fPiQy7r40UcfVfj+YmNjsXbtWkRGRiI2NhY///wzOnfuDACwsrJCREQEIiIiEB0djfXr16ttVVXFyMgIUVFRePDgAeLi4uDr64tHjx5xgWTixIm4dOkSzpw5g/j4eGzevBlZWVmwtbXF5MmTceXKFRw+fBjx8fE4efIkAgIClIYtFdHX18eYMWPg7e2Nu3fv4unTp1i8eDFatWpV4jmro6+vj+joaGRnZ5e575QpU3Ds2DGcO3cOCQkJ2LlzJwIDA1X+4ujYsSN69uyJ5cuX48mTJwgLC8PJkye5z4cMGYL8/HysXLkSMTExuH37NtauXYumTZuW+YwUZWdnw9vbGxEREUhKSkJgYCD09fXRrl07pf0GDRoEuVyOr7/+GjExMQgPD4e/vz/GjBmjsiW3PM/txYsXSElJUfn5uHHj8PjxY2zZsgVxcXG4cuUKNm3ahFatWlX4WZKSKD5RfCqO4pPyc6P4pBkUmyg2FUexSfm5UWxqIGopKZdGuLi4MAsLC+6/Xr16sWnTprHffvtNab+MjAz25ZdfMjs7O2Zvb8/mzp3LUlNTuc///PNP9n//93+sS5cubMCAAezixYuMMaaU+ZAxxi5cuMA+/fRT1qVLF9avXz92/Phxxhgrkfnw5cuX7Msvv2S2trasW7dubMmSJezVq1cqz8mY6gyGRdLT09m8efOYg4MD69q1K5sxYwZLTk5mjDGWl5fHli5dymxsbJijoyM7ffo0c3NzU8p8WDwzpOI2sVjM5s2bx2xtbVmPHj3YnDlz2I4dO5ibmxu3f1BQEHN1dWVWVlZs9OjR7NGjR9xnISEhbODAgaxz585swIAB7Ny5c2p/VhKJhH3zzTese/fuzMbGhn3xxRfsxYsX3Ofjx49nW7duVXv89u3bmZWVFfPx8VHKFKju+KNHj7J+/fqxLl26sCFDhrDr16+rPXdmZiabPXs269q1K3N3d2c7duzgMh8yxtjff//Nxo8fz6ysrFjv3r3Zxo0blX5+6p6RYuZDmUzGNm3axBwdHVmXLl3YsGHD2L179xhjrMT9PH78mHl6erIuXbowJycntmvXLiaTyUqcs4i6DKCMMfbgwQPWu3dv1r17dyaXy5UyYxb55Zdf2PDhw1nnzp2Zs7Mz279/v9LnFXmW5B2KTxSf1B1P8akQxSfNoNhEsUnd8RSbClFsaji0GKNVoQkhhBBCCCGENB4Nfmg0IYQQQgghhBCiiCrChBBCCCGEEEIaFaoIE0IIIYQQQghpVKgiTAghhBBCCCGkUaGKcAPh7u5eYn2+77//HpaWljh8+LDS9m3btmHo0KEAAEtLS/zyyy8AgIyMDG4dveKflceVK1eQlpZWqfInJSXB0tIS8fHxZe577949WFpaqlzkvrpJpVJYWlri3r175dr/8ePH+P3332u4VIQQQgipbfX9XUuRWCxGUFBQufc/c+YM+vXrV659GWM4efKk0jrShNRFVBFuILp164aHDx8qbbt79y5atmyJu3fvKm3/888/0b17dwBAREQEunXrBgDYvHkzrl69WqnrJycnw8vLC2/evKnU8e+99x4iIiJgampa5r62traIiIiAQCCo1LVq0uzZs/H8+XNNF4MQQggh1ay+v2spOnToEM6cOVPl86jy22+/Yc2aNVQRJnUeVYQbCAcHBzx69Ij7O2MMv/76K6ZNm4bff/8dMpkMACCXy/Ho0SMuOJuYmHALxVdlJa2qrsLF5/NhYmICPp9f5r46OjowMTGp0vUIIYQQQiqivr9r1dS5avPchFQnqgg3EA4ODsjKykJcXBwA4OnTp8jPz8eYMWMglUrx999/AwCioqIgkUjg4OAA4N2QHH9/f5w7dw7BwcFKQ1/++OMPDBkyBFZWVhg3bhwSExNVXt/V1RUAMGDAAAQFBcHf3x8zZ87EhAkT4ODggJs3byI1NRXz5s2Dg4MDunTpgqFDh+K3334DUHJotKWlJc6fP4/BgwfD1tYWEyZMQEJCAgDlodFFx/3000/o378/7O3tMXPmTGRmZnJli4iIwODBg2FtbY3p06dj3bp1WLp0qdpnuXPnTvTq1Qs9e/bEuXPnlD4r7R4mTJiA5ORkrFy5kjv/tWvXMGzYMFhZWcHe3h7z589HTk5OOX6ihBBCCKlL6tq7FgCEhYXBw8MDXbt2xbBhw3Dz5k1u/6dPn8LT0xM2Njbo3bs3fH19IZVKERQUhJ07d+KPP/6ApaWlymulpKRg+vTpsLGxwfDhw5GUlKT0ubr3m6SkJEycOBEA0LlzZ9y7dw8FBQX49ttv4eTkhM6dO8PFxQXff/99RR8/IdWOKsINROvWrWFqaooHDx4AKByq061bNwiFQtja2nJDdv78809YWlqiadOmSsdPnToVAwcOxCeffIKzZ89y20+fPo1ly5bh7NmzyM7OxqZNm1Rev2h4zalTp+Du7g6gMEh+8sknOHbsGOzs7LB48WJIpVL88MMPOH/+PFq3bo3Vq1ervaedO3di+fLlOHr0KNLT07F161a1++7duxebN29GQEAAHj58iAMHDgAAEhMTMWvWLHzyySc4f/48rKyscOLECbXnOXXqFI4ePQofHx8cOnQIgYGBSp+Xdg/+/v5o3bo1li5dihUrViAxMRFz587FmDFjcPnyZfj5+eHu3bs4efKk2usTQgghpG6qa+9aT548waJFi/D5558jODgYo0aNwpw5c/D48WMAwKJFi9ChQwcEBwdj+/btuHDhAs6ePQt3d3dMnToV1tbWiIiIUHktLy8vyOVynDlzBtOnT8fRo0e5z0p7v3nvvffg7+8PALh58yZsbW2xb98+XL16FTt27MCVK1cwbNgwrF+/HikpKZX5MRBSbagi3IAoDtm5d+8eevToAQDo0aMHF5wfPHjADdVRJBKJIBQKoaOjg+bNm3PbZ8yYgV69esHS0hIjR47EkydPVF676JhmzZpBKBQCAIyMjDB+/Hh8+OGHMDAwgIuLC1atWoWOHTviP//5Dzw9PRETE6N2CM2kSZPQq1cvWFlZYezYsUrDkYqbM2cOunbtCgcHBwwePJjb98yZM+jcuTPmzJmDDh06wMvLCzY2NmrPc/r0aUyYMAEuLi746KOPsHbtWqXPS7sHIyMj8Pl8GBgYwNDQEDKZDCtWrMDo0aNhamoKR0dHfPzxx3j27Jna6xNCCCGk7qpL71oHDhzAiBEjMHToUJibm2Ps2LHw8PDAsWPHABTOKW7WrBnef/99ODg4YN++fXB0dIRQKIS+vj4EAoHKqWbR0dG4f/8+1q5diw8++ADu7u4YM2YM93lp7zd8Pp9rAGjRogV0dHRgYWEBb29v2NjYwMzMDDNnzoRMJqOcKkTj6l62IVJp9vb2OHPmDORyOX7//XfMnj0bANC9e3fs3bsXMpkM9+/fx1dffVXuc5qbm3N/NjQ0RF5eXrmPbdOmjdLfx44di0uXLuGPP/7A8+fP8ddffwEAN6emtGsbGBiUmiVa3b5Pnz5Fly5dlPbt2rUrsrKyVJ4nJiYGM2fO5P5uYWEBXV3dct1D8eRd7dq1g46ODvbs2YPo6GhER0fj2bNn8PDwUHsfhBBCCKm76tK7VkxMDKKiopRGrxUUFMDa2hoA8OWXX2L9+vU4deoUnJyc4OHhUeKdSJVnz57BwMBAKYFply5d8NNPPwGo+PuNm5sbbt++DV9fX8TGxiIyMhIAKJkW0TjqEW5AHBwc8PjxYy6jYadOnQAAVlZWAAqz+CUkJHBzVsqjePKqiiRAUKxAyuVyTJ06FQcOHMB7772HadOmYePGjaUer62tXe5rq9tXVfKtsu6h+OdF56joPTx58gQeHh6Ijo6Gvb09vL29uWHjhBBCCKl/6tK7lkwmw7Rp03D+/Hnuv5CQEGzZsgUA4OnpifDwcMyZMweZmZn44osvuGHLZSleBsXG/oq+32zbtg0LFy4En8/HZ599hlOnTpWrDITUNOoRbkDatWuHpk2b4uzZs+jWrRt4vMJ2Dm1tbdjZ2eHMmTOwsLAoMWeliJaWVqWvXdaxz549w2+//YZbt26hZcuWAMDN1a3J7IIffPBBiTWA//77b5iZmand/9GjRxgwYAAAID4+nlumoKL3cOHCBdjZ2SnNbY6Pj0fbtm2rfmOEEEIIqXV16V2rffv2SExMVHqv2LFjB4yMjDB69Ghs2rQJ06ZNw4QJEzBhwgTs3r0bwcHBmDt3bqnlsLCwgFgsRmxsLDp06AAAXC8uUPb7TfFz//DDD1i1ahUGDRoEANwUMcouTTSNeoQbmG7duiEkJISbs1Kke/fuCA8PVzlnpYi+vj5evHhRqeQF+vr6AApbCcVicYnPmzRpAh6Ph0uXLiE5ORlXrlzhWiXz8/MrfL3yGjVqFP766y8EBATg+fPn2Lt3L37//Xe1vwA8PT1x/PhxXL58GVFRUVi5ciX3S6489yASiRAbG4tXr17ByMgIUVFRePDgAeLi4uDr64tHjx6hoKCgxu6XEEIIITWrrrxrTZ48GVeuXMHhw4cRHx+PkydPIiAgAObm5tDV1cUff/yBdevWISYmBk+fPsXNmzfRuXNn7lxpaWkqM1R37NgRPXv2xPLly/HkyROEhYUpJfos6/2mqJyRkZHIy8uDkZERrl27hsTERPzvf//D4sWLAdTs+x8h5UEV4QbGwcEBb968KRGce/ToAYlEUmpw/uyzz5CQkIAhQ4ZUuJWuWbNmGD58OBYuXKiUCbFI69atsWbNGhw6dAgeHh7Yu3cvVq5cCW1tbS67YU1o06YNduzYgXPnzmHw4MH4448/4ObmVmIodZGhQ4fCy8sL3t7e8PT0hLOzM0QiUbnvwdPTk2v5nDBhAuzs7DBlyhSMGTMGycnJStkcCSGEEFL/1JV3LRsbG2zevBmnT5+Gh4cHDh8+DB8fHzg7OwMoHJKcl5eHUaNGYdy4cTA1NcWqVasAFC7BxOPxMGjQIGRkZJS41vbt22FsbIwxY8Zg27ZtmDBhAvdZWe83FhYWcHR0xLhx43Dz5k34+PggKioKHh4eWLJkCT799FPY2Ngo9TIToglajMYlkAYsKioKUqmUm8MDAP/9739hZWWFuXPnarBkhBBCCCGEEE2hHmHSoCUkJGDy5Mm4ffs2kpOTcebMGdy5cwf9+/fXdNEIIYQQQgghGkI9wqTB27NnD06dOoWMjAy0b98e8+bNg5ubm6aLRQghhBBCCNEQqggTQgghhBBCCGlUaGg0IYQQQgghhJBGpU5UhDMyMtC3b1/ExMRouiiEEKKE4hMhhBBCSMMj0HQBCgoK8PXXX0MoFJZr/7x8KSS5FV+HlcfjQS6XV/i42kblrF61WU4pAKEOH7oCObQAaMnzwVhhWxPLl6kvI58HuawePMsaLKe2YdMaOW9VVSQ+VTY2AfR9qk71oYxA7ZdTMT7xFGIT8nLBtFQvJwdQfKqrsamiqhKfalN9+f4WR+WuGKV4JMmGHDrlPra+xKTiaqLcDSU+aZLGK8LffvstxowZg++++65c+0tyCxDxv9gKX8fQ0BDZ2dkVPq62UTmrV22WM1MKfNC+Gdo3l0AHMmiLE1FQULiovOzFK/VlbGKI7Nf14FnWYDlNHAfWyHmrqiLxqbKxCaDvU3WqD2UEar+civFJJI7jYhMv7ikKdFqpPa6xx6e6GpsqqirxqTbVl+9vcVTuilGKR3+FIZdnXu5j60tMKq4myt1Q4pMmabQiHBQUhObNm6NPnz7lrgjzeDwYGhpW+Fp8Pr9Sx9U2Kmf1qs1ySnKl0NPTg54eoC/gQUuuD6Gw8NqyJup7hPk8Pgyb1INnWU/KWV0qGp8qG5sA+j5Vp/pQRqD2y6kYn/QUYpNcTw9Ckfpy1JfvfX0pJyGEkLpDoxXhwMBAaGlp4c6dO3j8+DGWLFmCPXv2wMTERO0xcrm8Uq1X9aW1jspZvWqznBIpIJEIIZFIIIMM2pI3KHg7Ek1WSitgfWndrMlylm9iRO2qaHyqbGwC6PtUnepDGYHaL6difOIpxCaeRIICGcUndepibCKEEFI9NFoRPnHiBPfnCRMmYM2aNaVWggkhpLZQfCKEEEIIabg0PkeYEEIIIYQQQkjtKpQwSGQAACAASURBVCgoQEJCAnJzczVdFLWEQiHMzc2hra0+sWNl1ZmK8LFjxzRdBEIIUYniEyGEEEIamoSEBPD5fLRs2RJaWlqaLk4JjDGIxWIkJCSgY8eO1X7+OrGOMCGEEEIIIYSQ2pObmwuRSFQnK8EAoKWlBZFIVGM91lQRJoQQQgghhJBGqK5WgovUZPnqzNBoQgghhBBCCCF1S2xsLHbv3o3c3FxIJBL06tULtra2uHDhAtauXavp4lUaVYQJIYQQQgghhJSQnZ2N1atXw8fHB2ZmZpDJZFi5ciVatGih6aJVGVWECSGEEEIIIYSUcOvWLdjb28PMzAwAwOfzsWrVKjx69Aj3798HAJw9exY3btyAVCqFgYEBfHx88M8//8Db2xsCgYA7RiAQ4OuvvwZjDFKpFIsWLaqRJFjlRRVhQgghhBBCCCElpKen4/3331fapq+vzy1nJJfL8fr1a/j5+YHH42HBggV4/PgxoqOjYWlpiXnz5uHBgwfIzs7Gv//+CwMDA6xZswbPnz+HWCzWxC1xqCJMCCGEEEIIIaSE1q1bIyoqSmnbixcv8OeffwIAeDweBAIBVq9eDX19faSmpkIqlWLQoEE4ceIEvvzyS4hEIsycORM9e/ZEYmIilixZAoFAgMmTJ2vgjt6hrNGEEEIIIYQQQkro3bs37t69i6SkJACAVCqFv78/jIyMAADPnj3DrVu3sG7dOixYsACMMTDGcOvWLXTt2hU7duxAv379cPz4cdy/fx8tWrTA9u3bMXnyZAQEBGjy1qhHmBBCCCGEEEJISSKRCCtXrsS3334LxhjevHmD3r17o23btvjzzz9hamoKoVCIqVOnQkdHBy1atEB6ejo6d+6MtWvXgs/ng8fjYd68eWjdujW+/vprnD59Gnw+X+M9wlQRJoQQQgghhBCi0ocffgh/f/8S2+3t7QFA5WcA8N1335XY5ufnV72FqwIaGk0IIYQQQgghpFGhijAhhBBCCCGEkEaFKsKEEEIIIYQQQhoVqggTQgghhBBCCGlUqCJMCCGEEEIIIaRRoYowIYQQQgghhJBGhZZPIoQQQgghhBBSq+RyOTZv3oxnz55BR0cHS5cuhampaa1dn3qECSGEEEIIIYSU6mxgIOx6fIyWrVrBrsfHOBsYWKXz3bx5E/n5+fjuu+8wc+ZMtesR1xTqESaEEEIIIYQQotbZwEAsWvkNRG5zYTa8E/KSIrFo5TcAgJEjRlTqnA8fPkTPnj0BAF26dMGTJ0+qrbzlQT3ChBBCCCGEEELU8tm4BSK3uRC2tYYWXwBhW2uI3ObCZ+OWSp9TLBZDJBJxf+fz+ZBKpdVR3HKhijAhhBBCCCGEELWS4mKga9pJaZuuaSckxcVU+pwikQhv3rzh/i6XyyEQ1N6AZaoIE0IIIYQQQghRy7RdR+QlRSpty0uKhGm7jpU+p5WVFe7cuQMA+Ouvv9CxY+XPVRlUESaEEEIIqSdkMhmWLVuGMWPGwNPTEwkJCZouEiGkEVi+eCHEYf7IjX8IJpMiN/4hxGH+WL54YaXP2bdvX+jo6GDGjBnYsWMH5s2bV40lLhslyyKEEEIIqSeuXbsGAPjhhx9w7949bNiwAXv27NFwqQghDV1RQiyfjVuQeDoGpu06Ys361ZVOlAUAPB4Pixcvrq4iVhhVhAkhhBBC6gk3Nzc4OzsDAF68eAFjY2PNFogQ0miMHDGiShXfuoYqwoQQQggh9YhAIMCSJUsQGhqKHTt2lLk/j8eDoaFhLZSsavh8fr0oZ3FU7oqR5Eqhp6cHPT1AV0cX2vrlLwOfx4dhk3r4rOtpuRs6qggTQgghhNQz3377Lb766iuMGjUKISEh0NfXV7uvXC5HdnZ2LZaucgwNDetFOYujcleMRApIJEJIJBLw8vOQKy1/GQybGCL7dT181jVQbmG1nq1xomRZhDRgQaHX0HfcZLR28kDfcZMRFHpN00UihBBSBefPn8fevXsBAHp6etDS0gKfz9dwqQghpP6hHmFCGqig0Gvw3eWPgx48OJobICIhB1N3+QMAhvd30XDpCCGEVMaAAQOwbNkyeHp6QiqVYvny5dDV1dV0sQghpN7ReEVYJpNh5cqVeP78Ofh8PjZs2ABzc3NNF4uQes/v0BEc9ODBpX3h19ylvQAHPaSYfegIVYTLgWITIaQu0tfXh5+fn6aLQQgh9Z7Gh0YrLgMwb948bNiwQcMlIqRheJqUBkdz5eFyjuZ8PE1K01CJ6heKTYQQQgghNe/vv//GnDlzav26Gq8Iu7m5Yd26dQBoGQBCqpOlqQkiEmRK2yISZLA0NdFQieoXik2EEEIIIe8EBZ6FUw87tGrVEk497BAUeLbK5zxx4gR8fX2Rl5dXDSWsGI0PjQYqtgxAZZcAqC+p7amc1as2y6m4HIC+gActuT6EwsJry5rI1B5XUyn1l3wxA9O2bMYBdykczfmISJBh2iWG1QtnVOp6jTH1f23EJoC+T9WpPpQRqP1yKsYnPYXYJNfTg1Ckvhz15XtfX8pJCCH1VVDgWWxYtRAHBwKO4wwQkZCBqasWAgCGjxhZ6fO+//778PHxwdq1a6urqOVWJyrCQPmXAajsEgD1JbV9Qyvn9fAQHD+yF6nJcWjZph3GT5oBZ1ePWihhodp8norLAcggg7bkDQoKCj+TlZIyv6aWAnB37IVcyRzMPnQET5PSYGlqgiVfTIK7Y69KXa8mlyyoy0sA1HRsAhre916T6kMZgdovp9JyJQqxiSeRoEBW+/GputVUOetybCKEkNq0faMPDg6Ecu6ZgVLM3uhTpYqwi4sL/vnnn+oqZoVovCJ8/vx5pKSkYMaMGbQMQANzPTwEe/Zsh+GAuTAz7YS8pEjs2bMdAGq1MtyYDe/vQomxKoliEyGEEFKSpjs5iGY8jUuC4zgDpW2O5nw8jUvSUImqTuNzhAcMGIDIyEh4enpi2rRptAxAA3L8yF4YDpgLYVtraPEFELa1huGAuTh+ZK+mi0ZImSg2EUIIqauuh4dg+sQhGOJqjekTh+B6eEitXXfPnu2A43SYLQwCHKdjz57ttXZ9ojmW7UxV555pZ6qhElWdxnuEaRmAhis1OQ5mpp2UtumadkJicpxmCkRIBVBsIoQQUhdpcsSdYicHgML/v+3koF7hhm3+4uWYumohDg58l3tm6mVg2brlmi5apWm8IkwarpZt2iEvKZILlgCQlxSJlm3aaa5QhBBCCCH1mCYro9TJ0XgVzQOevdEHT+OSYNnOFMvWLa/S/OAi7733Hvbt21fl81SUxodGk4Zr/KQZyP7ZH7nxD8FkUuTGP0T2z/4YP2mGpotGCCGEEFIvpSbHQVdFZTS1lMpodQ2lLurkUNRQOjmCQq+h77jJaO3kgb7jJiMo9Jqmi1TnDB8xEjfv/YGUlFTcvPdHtVSCNYl6hEmNKWqVPH5kLxLfJlSYNWt+nRs6Q0kfCCGEEFJfVHTEnbqh1EKhED17u1bo2uMnzSgchj1gLnTfniv7Z3/MmjW/KrekcUGh1+C7yx8HPXhwNDdAREIOpu7yBwBKOtqAUUWY1ChnV486XamkzNaEEEIIqU8qWhlVN5T6wD7/CleE60snR0X5HTqCgx485aWBPKSYfegIVYQbMKoIk1pTF3teKekDIYQQQuoTVZVR5z7OOH5kL7b6LCvxjqV2Xm9ibKWv39DekZ4mpcHRXMXSQElpGipR7WGMQUtLS9PFUIsxVmPnpoowqRXl7XmtamW5+PHTZ3iV2trZUJI+BIVeg9+hI3ialAZLUxN4TZlELZiEEEJIA6VYGS3rHUvdUOpWZh3KvM718BDs37sdWen/QktbCENDQ3w+a2GDqwhbmpogIiGH6xEG3i4NZGqiwVLVPKFQCLFYDJFIVCcrw4wxiMViCIXCGjk/VYRJrShPz2tVhymrOn7bdl/Mys1Ve3xDyGxN81oIIYSQxqusdyx1Q6kXLFha6nmvh4dg587NMBq4AE3fHpd+aRv8t28A0LCmkHlNmYSpu/xx0ENhaaAQOZbOnqTpotUoc3NzJCQkIDU1VdNFUUsoFMLc3LxGzk0VYVIrytPzWtVhyiqP71/68Q0h6QPNayGEEEIar7LesdTN6+3/6VBkZ2erPe/xI3thNHCB0nuVsfsCZFzxb3BTyIrel2YrjK5bOrvhj67T1tZGx44dNV0MjaGKMOHU5Bze8vS8VnWYcmWObwhJHxrzvBZCCCGksSvPO1Zl5vWqe6+SZqUgNatKRa6Thvd3afAVX6KM1hEmAN4NK4bjdJgtDAIcp2PPnu2VXmeuuLLWFL4eHgK+UFSltekU17YTR97AiwNfIGHLMEBbF54j+1XbvdQ1hfNaZErbGsO8FkIIIYSU/Y5VWerWDBY0bVWvppARog71CBMANZ89ubSe16JKuL6NO9Iv+8F4oFelhikXDXOWWDpBHHld6Tzq5rQ0hOWTGuu8FkIIIYTU3Oi28ZNmcHOEFd+n+LICjJ9Uf6aQEaIOVYQJgNrJnqxuWI5iJVzH2ByZYQEoyEiEQGgAry9XljuQF+3nt3U9jD9bVuacluvhIfDbuh4tiu1b35ZPaqzzWgghhBBSqCaWNCo63/6925GimDV67rJ6845ESGmoIkwA1Hz25NLmHytWwkWd+kLUqS+YTIrELcMrHGidXT2w1WcZdMuY01LUEyzNzVG5b31bPonmtRBCCCGkujWUNYNpmUmiCs0RJgBqbn4JUPb8Y3VzUCpbCS/PnJbjR/ZCYOkELR39ar02IYQQQkhlXQ8PwfSJQzDE1RrTJw5psPlNalPRMpO7XHKQu8IAu1xy4LvLH0Gh1zRdNKJh1CNMANRs9uTKrG/36vI26PK1MMTVutwZrIt6nVOSnkMraD1YgQTazc2gZ9EL4r+vKs1pSU2Og0AihaHdIGRc9kMLhfnEacEb8anbwCrfNyGEEEJIeTWEvCV1ES0zSdShijDhVPfwF8WKqXkF1rdr0qIVmLQAegMXw6icvwiKfnkILJ3AzxbDeKAXpNnpyLp9Eq/vnIaWthB8gQBbfZZh354t4OnqoSAjEYgC9Do6vJ2XnATt5qaQi7Nw/dZ1fNTFhn7xEEIIIaRW1HTi0saKlpkk6lBFmNQIxVZN7dCACq1vN33iEAjdlH8RSCyd4Ld1Pbb6LCusKMtlyH6ZzvUWF/3yyAwLgPFAL8jEL5EVcUKppzf90jaILPtAHPsbjIeu4LZnXPaDkdNEiDr1RW78Q2SGBcDQbSb94iGEEEJIralq4tLS8rE0ZoXLTOZwPcIALTNJCtEcYVIjFFs1m348GhmX/UpdQ1hxPkxK0nOlBFbiyBsQR15Hi8+WwWxhEIRucyGWMjR3n8/NN05JLjymICMJuqadkHXnFFoM9IKwrTW0+AIuc/SbJzdh7L5AaXuLgV7I+uUUcuMfIuOyH5r2Gg1d005IrWcJswghhBBS+0KvnK+Web1VyZlSVj6W+igw9Brsx81CK6dBsB83C4GVnNPrNWUSpobIce25FAUyhmvPpZgaIofXFFpmsrGjHmFSLsVbGafP8ELP3q5q94FAF82z0wEUZoIGgMzQwmWRWpm2L7GGsOJ8GH7wJry6fRLNnCYAALLunILx20ot8G45pMywALw/bTcwYC7yL2xAXlIktFuYIi8pkqsQK9I17QR5rljl9oKMRGSGBSj1DCv+4qFWVkIIIYQUdz08BHsCtsOwf9Xn9VYlZ0pDG1YdGHoNi3YehWiAF/dcF+30g1BPDx6OPSt0rtKWmaRs0o0bVYRJmVRVVrdt98Ws3FylNXmL75N6wRevIk5A9joN2i1MoWfRC/rRBdh/9CJ3blWB23jwIqSd94Ze265cJVV15TUJ4sgbyPrlFKSSHKQHb4LIegDSL/tB0LSVyuHYWrr6KrfzhPpo7jYTuqaduB7rWbPmq703Sl5BCCGEkONH9sKwf/VUQKuSM6Wqw6rrGp9DpyEa4FXsuXphTcB3Fa4IA6qXmSzKJn3QgwdHcwNEJORg6i5/bn/S8NHQaFJC8aHK+/du5yqrRcOJDfsXBvkiihVaLb4AMvFL8LR1YTzQC+YLg9DcbSZyHv6MnOzXSsN0UpPjVPfc5r0BIvYjcctwCIQGKocK8ZuY4NXNo2jefybMvzoHkfUA5Nz/EbKsVMjEmUi7uFFpOHb6pW0w6OxSYph22sWN0NXWxutgXyT6jUXKDyugrfDNKH5vwrbWMBygfP81ISj0GvqOm4zWTh7oO24ypfknhBBC6hh17zHlmV6laqkkZ1cP7D96ERfDH0Io1EOzwYvL9f5R3UtRalpSkupOkISEhGq7hmI2aW2+1tts0jz4HTpSbdcgdRv1CBMlqno/s35YgaZltDIWb4nMunOKm4sLFLbkmQxehNRz3ti3ZwvXklkUuIv30LZq057rOebmvQyYy2WClr76F1q6+jC0G8Qd28xpAvTadgUi9mP/0Yu4Hh6CE8f2ITExFhDooPmAL2DQpR/EbT7iskRrvd0uMDRGevAmGHYbCKPeY5VaXTXRykqtlIQQQkjdp+49pqwKaHlGm1Xk/UPVsGrF0W31jampmcrnam5uXm3XoGzShHqEiRJVvZ8Co9ZltjIWb4lUN0eX5b1BtjgHg12tMMyjF1KSniM9eJPKRFpFLaVbfZZBmwe8DvbFy2sH0eLTuTD/6hxaDlsBceT1t8m0buDFgS+QcmolUlP+4VpVfwgMx8Xwh2jZ6n0IDI0BFM5Zfn/abrQavR6Cpi1h0KUfNyRbEn2nRKurJlpZqZWSEEIIqfvGT5qB7FB/tQlB1SnPaLOKvH84u3oUVnrfjqZDxH4uH0tVqOq1rg3Lp4yC+GflEXzin/2wZubEartGYTZpmdI2yibduFCPcCNXPAlU0Zq/4sgbyLpzCgUZSeCLmiPt4kaYDFn8rpUx1B+zZr5rZSzeEqlqju6r2yfBExnBZPCid0saXfaDTsv2SDvvDXneG7Rq055rvSzeUpoTvAkGXQcozyce6IXUc95geW8gMGqNFu7zITA05lpVBw8do7J8Rddu5vQuoOqadkJBeiJeHPiCW1O4ICMRC1f41norK7VSEkIIIXWfs6sHhEIh9u/1Q+LbdynnPs44fmQvtvosU5vgqjy9vRXt5VVcirI6aDJHyoi3o998Du1FYlIiTE3NsGbORIxx74/s19nVcg2vKZMwdZc/DnpI4WjOR0SCDFND5Fg6m7JJNxZUEW7E1GVsTrvgi4LU50pr8Kae80ZumD9SM1LQsk07LJi/VClrdPEED4bNjJF5aQuauy98F7z/+BEth60oUZHNDAuAydAV3JBmoHAtYVVJtDJDA7hs0sDbXub8NzD/6pzSmsBFrapFFeGi8m3d9A1YgQQ8XREMbD24jNbAu4p6UdKsvKRIpAdvAgDMmjWfu7eWbdpVSytradSteaevzWD72WgI+FpITM+mDIeEEEKIhvX/dCj3TlTeymN5hlQXf7dSfP8o6shISX4OgdAAUkkOWpm253qiq2OlC01noh7R34WrEKtTlazPpWWTJo0DVYQbMXUZm1OD1qPl8JVK21sOK6yoHg9/CAAwNDREdrZyi5xiwE59WxlOO7ce8nwJtFuYgeW/UZv9uagV9Hp4CHZv90Zufj7YqZXQbmGKpr1GQ9Spb+G+mYlKxxcumWSmtCZw+mU/tPl8L9eqqryskw5auC+AFo+PVzePcpmpCyvqwWg5bGWJ5+G3dT1kuWK0bNMOXy7fwN1jaS29VaWqlXLyBQkmWGsjJDoHhwbp0dxhQgghpI4pb+VRXW+vcx9nTJ84RKkSq7jaBqBc2TZXGOUmMXPA9s1rwQDI899Au7kZJGYOle7FVddrnZD0HJ4j+0GLx8frjBQYNjPm/lybS0xWRz4VVdmkSeNBFeFGTF2AY/kSlRXWspJDqWoFzbu8DUygg2ZuM5F+abvK1k9+E5PC7QZNsMV3JXhCA7Qc8TX3iyHjsh8AgC9qBi2BDpL3fg5pVgoETVtBlvcGLdz+q1ROWVYq0i74gi8Uwbl7O/B09WFgOwhmY7YW/rK4tA3N+k6GkdPEt0mzEsHTFYHlqb5vaW4OzBcW9jjv3LkZTFqAZoMXl2jpFcsY1v2wE7EJybBs/z6+8pqIoR6DK/xzAZRbKZ8kpqJDMy34ugrhfSsPhz/T43qKC+cOSzH70BEK5IQQQoiGlTfBlareXuc+zrh+63qZvckqOzIGeiE9ZCuYQEdpClrGZT/od3Iudy+uYucBXyhS+94mljIYu8+FIDsdr24dg7H7XDSt5eHTivlUAHonIhVHFeFGTN2wHIFe4XJFMvFLbp6woGkrGDYzLvV8qgKz0cAFyA3zByL2QyZ+hfRL22DsvuDd0ONL2yATv0bK6dXgCUXQehvAFc/RYqAXMq74g1cggUDPAM0/VZjnG7JFqQyFawUbIO/FE5h8tozbL+3CtxBHXofsdRoETVshM3wfTL84DL6oGdIvbYNRnwl4deOIyueh2ONsNHABMq74l2jp3b9nM5po5eCIh9bbVsmXmLZ2G2QyHYwY8km5fyaBodfgc+g0kt7Oh1k+ZRJmr9uMx7MNoM3XwvhzEjia85WOobnDhBBCSN1QkSzSxef0qpoWpqo3WV1lW54rRssRq0q8Q2WGBiA7M6nMshfv0Ei74Iu04I0wGbxYuXOCyWHs8SWEba3x4sAXJVYJqa3h05RPhVQVZY1uxMZPmoHsn0tmOhwwYBBeBm/EyxuH0dxtJswXBqHFp3ORJ2NK2QKLZxJMSXquskf1dUYK9h+9CC25FEZ9JiAzLAAJW4YjMywARn0mAPJ88PREMOj6CVie6uHT0lf/QiDgo7n7QqUMi8YeC/Hq1nGltYIBOUwU1t2TiV9CS1uHW9O4xadzAQAJm4chN8wfIoEWMi9tB8t7g/RL25TXHr7sh6a9RiuXJSulRPlkOWk44qGllOX5gDsP23aUf63hwNBrWLTzKPJ7z4DZwiC8Nu2BJRu3gjGGj3bl4OSjAnxkzKMMh4QQQkgdpe7dSnE1DHUZmMu7JrFhM2O82D8L8RuH4MWBLyCOvIG8pEiwglzVU9AyE8u10oVih8abp7dRkPocBtafIDM0AAlbhiH1nDf0OjpAlpPJXUfdKiHlWUe5qijrM6kqjfYIFxQUYPny5UhOTkZ+fj5mzZoFV1fXsg8k1aK0JAy3b1+H0K1k725RC1/olfMqE229un1SKZlVXlIk+EIRhrhagy8UQWBojPen7eY+z41/CO0WZmjuNhOZoQHgqRuGI9THm6xMGKupJCdsGQ7tFqYw6jMBGSHblIKyyjWNhyxGauBaCIV63FyWIa7WaNZnFrfGcOGQauWEWnlJkRA0baVUhrykSIjzobqnNl650lwan0OnIRrgBWFba2SG7oZ+1BWcHCVUmiPs8YEAky9IcPgzPcpwWMMoPhFC6iKKTXWbuncroORqGMWHEJenN/l6eAjyZAwtBs5VGl0nL8gH36C5yuN5uvplLucEKPc0Z905hRYDC99Jit7rcuMfIjMsANotTLnrKP5ZXZlrCmV9JlWl0R7hixcvwsjICN9//z327duHdevWabI4jZKzqwf2H72Ii+EPsf/oRS4Yv85IKbWF78A+/xLr3xkPXoSc+z8qtYKmBW+EVJIDQXNTaJt2LrFmcMbbHteiFktRJ2ekFdsnPXgTdPh8tesZaxuboe3ii3h/2m4IDI2hpaNXvjWNpfmQmDlg25a1GOxqBb5QhILMZDTtNRraLUwhzxMj+48f8fLmMa4sry5vA69AUqKl9z2TZqpbJdsWVpoDQ6/BftwstHIaBPtxsxAYeq3EzyIpKRGy7DS8ChiHnD8uwUCb4d8cxvUwH/5MD8ceFkDGN8CUywIIvXMw+5oBls6eS3NhagDFJ0JIXUSxqe4JvXJeqacXQIl3q/KsG1xab3KR40f2wmjgAuX3L/cFgDQPTC4r8Z6VHrwJn34ypFzDlBXXLVb37lSQkYgmPUZyI+gU/1yRdZSrw/D+Llg6ey5mXzOgdyJSKRrtEf7000/xySfv5k/y+fxS9ia1qaxWyZTEWNXzU/LeABH7kZD8HFo6ejC0Gwyj3mO5eSU8wxZIDVoPVlCYSdrIaSJEnfoiN/4htHT0oG/xMSQJjwr3yZdAS0cPpm3aICkuBs3dpyPjsp/Ssk5pwRshF2fhxf4voGfRCzkPf4ZAiyH7Z/9S1zQuSvbwJvI6TIauUD5ffh5ajVgFaXY6sm6fxOs7p5H9+wUYGjbBnDlfASjZ0qsFhkkB3+CIQqvktEtyLFsxA2cv/oxFO49CNMCLawVetLMwAZjisgDNmzWDIGIXTn8mgKO5YeE5LkoAAGOttOFozsebAi3EXjhVIz9zooziEyGkLqLYVLdcDw/BnoDtMOxfeoKr8iTRKm2kXlnnYdI8CJq2REF6Ivee1apNe8yfv7Tcc3UVM1lrN1fd0ysQGiDz0nYYNjNGbpg/lzVacYnNml5iUhFlfSZVodGKsEgkAgDk5ORg3rx5mD9f9QLhing8HgwNDSt8LT6fX6njaltdKef0GV7Ytt0X6K+Q1j/UHwvmL4WhoSFam3XgAqQ48sbbpFqJEAgNMH2GFw7s8wf7eKrScGT9Ts7IefgzhG2tkffiKbdeb2HP8SawvLzCpFl6Bmg5fCV33eTgjWCMIev2Seh1dCgcupyeCC1d5Yp2WvAmyPPeYNnqjQCAXTu+RUraP4BAB+khW2Ds8W5N44zLfoBchhZvkz0UldFk8GKknvPGm6hfIIn5TanSnR3qD6FQiP6fDuXWJ1aUz9fBjGPbEBOfhI86tMHa9dMwevhIfNDzM27Ic9F1MMALvke+w+QRQ3D6cji+3nMYeTkZODNGXyn74YEheph7ORdjrbQRkSDDh+atYNhEM/8++Dy+xq6tCRWNT5WNTUDd+d6XpT6Usz6UEaj9ckpypdDT04OeHqAn14dQh8ypngAAIABJREFUWHhtuZ4ehCL15agv3/v6Us7qUJvvTrWtvnx/FZ04tg+G/UsmuDpxbJ/Su0IrhfemInlJkWhl1kHpngcPHaPyHaOs82jp6HPvVUXvLNNneKH/p0PVnqv48x48dAyEQiEO7PNHQUYi0n/cBONBi5TOuWT5+lLPWR6K8UhXRxfa+uX/mdfX73p9LXdDp/Gs0f/88w9mz56NcePGYfDgspeakcvlJdavLQ9V697WRXWlnD17u2JWbq5yq+TM+ejZ2xXZ2dmY+vlcbNvmC4mlE8SR12GsUGHcts0XbzL/hfko5RZLSdQdLiN0ZmgAUs95g+W9gZauPkQf9YXoQ0ekBq0vkTXaZPBiZIYGoHn/mYWVXfErrre5aN5K4X6LkBq4Fvv3+mH8pBk4dupn3L0dDv9t3sjOESPjin9h0i2j1jBymoj0H7eoHjKd/wbiyOswGbpc+Rdb/7n41mclcnNzVbZ02jsOwJgJo9G+uQQ6kEFbnIjs7GwkPI+D2XAV6/AlJOBw4EUs2LoXugKe2nnGj9PluPZcCs+gXKz+cjayX2vm34dhE8Mau7awRs5adRWJT5WNTUDd+d6XpT6Usz6UEaj9ckqkgEQihEQiAU/yBgUFhdt5EgkKZOrLUZPf++pUU+VsCLEJqFp8qk11+furuKyQ4lq56kbIJSbGKt2L54TP1a4bPHqYS4nzqqPqPGnBG8Hy3iAzLABNe40uzG3Sfy727/VDz94l548r3kuTFq3A5DJkv0znrr/v8Hml/VS9B1aFUjzKz0OutPznqy8xqbiaKHddjU/1iUYrwunp6Zg6dSq+/vpr9OrVS5NFISoUT+tflO0wNTkOrcw6wLmPM37++UcYf7asREto/oUNKnuMM0MD0PTj0WjefyZyEx6i+bAVSq2a6tYwLshM4iq7RZXi9Mt+0DE255JZFc37heN0bljS4KFj0LO3K/b4eeOnnwoXpJfnS8AXNVM77Ee7uRkKMhLVrim8fbsvHv/1J2Z5rSjXczRra67yOqamZli58yCM+Ln4fogAcy8XZoQu6hEGCucZ62sDw09LMMLdnYb/1CKKT4SQuohiU+0rvqyQ4vDn8i6XVN51g8t6x1A8j7ppaACgb9m7xNrF6u4l/dI2NHefDxgaKw3rLv4eWNYzUtVQQEhdptGKcEBAAF6/fo3du3dj9+7CTML79u2DUEhtHHWNqsB5/Wd/SCU5qiuMkhyk/LACPP2m0OJrw9hjgfKwZBQmYpBmp+PFgS9QkJEE7Ram0NLVV7OWryl37qJKsfFAL2SGBXAV4bykSPANmhcOnX75L/y2rodQKERubi7Cwi4DOvpoNXQFpNnpSLu4EfI8MdIuboTJEOX18fQ7OUP6R4baNYWbu83E5aD1AFCuyvAKr8lYuM4PGPCu11z8sx/WzJmIBd6b0KaJFtyO5eN9Ay1MPi/B4aHvMkKPDZSAMS1k5TL88ttvCAq9RpXhWkLxiRBSF1Fsqn2Kia4A5bVyx0+agT0B25WmkqVd3AgDoQ6uh4coVQbLs26w8eBFuHLeGx91sVFbkSw6z/SJQyAxc4Ak+g5e3z0D7Ram0O/kjKw7p8AXNeMq44qVVL5QhBbFOjCM3RcgMyygcFWPSqwBXFpDAVWGSV2m0YrwypUrsXLlSk0WgZSTul8CkqD1qpMpGLXG+9P34MX+WWjxqfJxRYu78/Sa4NWtYzB2f1dJTj2/QeXi7UZOE7lzK1WKMxLBZNLCY4O8oaWtozRHZtt2XwiYDHJtPRi/LYc48gZ42rowGbIY0ux0bsg0v4kJRJ1dkBd5FUIBX2Ul2chpYmHPc4EEP4VdKfUXVZGRQwZA/lIMn0N7kZiUCFNTM6yZMxFaAJoKtbBv8LuK75izbzD1ggTxWQzmJk0g1xIgeKz2289zMHWXPwBQZbgWUHwihNRFFJtqX2mJrpxdPSAUCrHTzxcp6YXTr5q5TIWgWO9qRc4rz3tTZmX0engIUpKeg58tVpqeln7ZD7KsVG7Y9fhR/ZH1tlzN3ecj49I21SPvMpKU7qsiSmsoqO6K8KlLP+Pb3XvxNCkNlqYm8Joyid6JGqEHDx5g8+bNOHbsmNL2q1evYteuXRAIBBgxYgRGjRpV6nk0PkeY1A9qsxTmS5B+2U85CF/ahmZ9J0OLL4A061+16fe13lZGFQNny6HLkHJm9bus0W8TYulb9uaWW1KsFGtp6yFh8zAIjFqDJxTB2H1+iXm9qYHrwKR5XDkU18YDAIMu/ZAb/xCpgeugl/gbPp/zFZxdPXA9PAR+W9dDmptTIsM1v0lLGA/04oL89fAQHD2yF+nJcTBt3x4rl8zC2IE23D2P6O+ilCF69JfL8b+HfyI7Dxj6/+ydeWBU5dn2f2f2NZNlkqAmobZValTcurx8IPtiQMKmItZdrKClSH2httBFJWhBxDRaoKKotCAoARIgyCJBodhWXKgNLn2LJIGSZLLOvp7vj5M5ycnMoCAq6Pn9A5mZ88yZk8lznvu57/u61vrod56WbbdYeek6C3dX+slNN2MxmVhZFFGIZz03JsJ9K19QJ30VFRUVFZUviU8rfx5xzXhWLC/FdGOJ4jWfFgymGlefmS/bVSYjnoEVjBacRUoxTmfRTBrL5yvKrh3dNvS1aTknrLw7FQ/gVGvE2vrDckvdqZZLr9+xmwUr11FfX0dmRgYW0c0LY3UMKLApEgQApStfUAPkbwDPPPMMFRUVmM1mxePhcJhHH32UV155BbPZzJQpUxgyZAjZ2dkpx1IDYZWU9CylSapSaLRgLRwslSM31yPojFgvHkL7/rW4Ni9G0JtTHpesHzjidiFodIhhKQgWIxF8h16nY/86BKMF+5XXykGxa+sSQCCt3w1kDLyFIwuLk4tfhQOyB7Gpd9/UvsLhAA1HD1P6xHwWlzyIzmwj4vegsTgUCteurUsQwyEibhctRz9JWhL0wNzFaIO3M2FMoojJ5J//ikOH3mPjZIucCb6p3M81q7xU3mThP60iRquexvpGBhQoFQYHFGj5sL7pVH+lKioqKioqKidJd1uh7kJX06d3KXZ/FnukZOM++eRjOMfOTmjRMtf9Q/Hanmsyy+WjEd9cl2I94+fAgb9hHzmDqLeV/z7/M8LN9egcucQCnqQJjPSrb5F9h0cNv+akrk+ygL5t3xq01nQYMDWhXLrvoM8WDK/fsVthP9m+4nZeKNYlJAjuWLocIRbiuTGahABZDYa/fhQUFFBWVsacOXMUj//f//0fBQUFOBwOAK666ireeustioqKUo6lBsIqSQUOAEVw17ZvDa7KRYrJ2rV1CbaLh+CrqZZthur/eDv+//xDLndu27eGpspFZHc/rqoUMehD78xXTJzemj20vbGKnEm/VvTZmL99Fe53tqLRm6WguLMPJmPQ7WitGTSufxhvTTWIIsdWTCf96psVfcOCwUzU14Fr6xKco2elFMmKB+cYbQixGJbLR6PPPI/mV5+WyqfbGxTv27ytjJzzvsUzSxcTimlpWDsPfVaepNg4fAYPL1meNBA+cPA9Nk42Kyby1RPNjF/rY29tlDRHGqaiB/Fv/HVS8ax8pyq/r6KioqKi8mXxWfx9P6toVs9xD73/Lts2lhAL+tBn5ks6JR++zs3dguykAlcnyO7mnnc+jUc/IdPton3vXxRWkE0VCzHknE/zpkeJBryY0zKI+T00b12CPjMfa9+RVL9R/Zlav+Ik2yjwvLOZ7PFzk5ZLL/yMgfCClesU9pMdbW3MqNJwyBXjIqeGuVcbicREmts78IVhRpX02JRL9WoF3ZeA97/H0RqNp33c1mCA++67T/558uTJTJ48Wf551KhR1NfXJxzn8XgUdmBWqxWPx3PC91ID4W84qQQOdESxj+wqMzY4C/CIIo3rH0YMBxEMJsRQgEDtwS5v3+b6hHLnuL1R08YFxIJeNEYrsYAXjcmKqaCvIkhue+PPOEfPUlonFc+hsXw+iCJRTzPatGwQRSLtTbRWr8T8nR8i6E0JO5tiLIrO7sS1ZQliNAyxCDFBpOHl30I0Igf1EbeL9n1rpB5hWybpw++Rjtu6BO/7u9AYLRANce7UpQjarj8XMRoh0nacqwbdyLYdVQm9xI4BP6bxSF3Sa+4OikltktxBuKkihn7g3RjzCmkIRLlpU4TV4+gSzyr34xMtp/17oKKioqKiopKaT1NQ/ixZ42RMnzmXiy65XE5ImOv+wc09guxkPbjOopm4tjxBc1WpItCNv+efX1hO8741ilawqLcVjcGM/+O/4XD24qe/ms+K5aXYix5QBNOB3pedVH9vso2CWNCXNFt9Mv3H9fV1cpbdW7ObHJuGsiKTvCa6faMfX1ik4sauCru7KvwAXFeoUyvovmC0RiO121877eNeeOMkysvLT/o4m82G1+uVf/Z6vZ/qS64Gwt9wUgkcNK5/hHR58tlD2+svKoI919YlpI+Yjs7upLmqFPN3foAYCRNpS+wJTu8/hY7969A6chQBa1PFQmK+drkfGEjh6+tHMFoQdHqco+/vOocti/G8v4vc636boH7YuP5hNBYHsUiQ3OsfUrynGPYTC/poeOUhNAZzUkEs5+hZNLz8O2IBL4LOlHTH1eHsxYEDf0voc84qmknztjJ0RguvVGznuuKRis9kN5I002s3QrsniPbVxTRXLsZmEGjR5zJhXT3uoMhFTg1Tr9Cz4m2fqh6toqKioqJyBvFZssYnOjb+uniV3hMLfilX6aUqu456mskaPUsW/XQ4eynec3HJg/K6Kr6W6x40P/b73xH1tFGQImA9GUukZIrYJ5sh70leXlflYGT/KtZNNCmq6Z4fb+buSr/isWeLzcyoCtDLJtAnL3VvqMrXj+985zscOXKEtrY2LBYLb731FnfdddcJj1ED4W84KUWwwgGC9TVEva207FhKLOilZecyTAV9CdQeJOp20brrT2SOmI6lcDCeg9vJHjubhlce4tiK6XIZsaPfZLTWDASDGVvhIII7n6SxuQlHVjb2y0bS8fYWcibMxZhXyLEV01OWLGv0JpzXPqAMeMc8QOP6h5MHz5EQgqAhZ9yDCRnm5m1lxMIBNIImYcysopm4qkpJH/BjNEaLrCzt2roE68VD8X+0n3BLHYLBjEmno911POkNJNJ2nLR+N/DA/GUATPj+D+Xn3UG4qdzP6olmRY+wPwTnpMHz4yzd7JPqmfoDPfOHdtliDDtfp5b7qKioqKionGGcjO9uMpJV6T311OMIOiO1iyegz8qX2q8KBxGsr0FjtNK8dQkao4XR425UWDoOHjaGFcuflNdVPYVCTb374rx2No3ljyRde2mMZp566nHSi2adkiXSqWbIu/OrO25g9lOS/WR7cxMDCmyK5wcUaDncJiY8dsgV484tMR6877bP/F4qZy+VlZX4fD4mT57Mgw8+yF133YUoikyaNInc3NwTHqsGwt9wUvW0OJy9aK1cSFSrJ3v8r+R+33jA2z2DKkajZI+dje+jv6IxmMm6ZoYicxwLhxBDPsyHKlhd3CVkcFNFBe3BgPze6VffLPfxKsYP+oimKLERw8HkwbPOQNTbSsPaeWhtmaDREu1oQp+ZR6RNshFIlr025hUSbW+kZecycibMk8cN/fejhM/u2roEwRilbd8auQQ8/v5aRw4ZA28h0PsySkr/xIRVP6R8x25KV74gjRcVGbPaRyAiZYgjUShIl6yUuu9srpkk7WzOH9p1jqpgloqKioqKytePZFV66UVSxjdv6lK5NzjkqsVXU03miOmym8WBvSsSxpt6z/1yMJpSKDQUoHFDiZyUkLVcNHrSi2YlVAx+1pLpE2XIWyKf7XrE3TYWrFyOVS8mVNM9tCdImhG0D3fIPcO9bAIOk5YH75uhJgy+xuTl5bFu3ToAxo7t0uMZOnQoQ4cOTXVYAprTfmYqZxU333YP7u1lBI4cRIxGCBw5iHt7GVPvuR+dTiv37ApaHf6P95M9drb8czyDGgt6MOYV4q2plsuE4887R89Co9VhMwqsLtYw5Hwdeq0gCUQVa7CbuiY0a+Eg0q++hcb1D1O7eCJNmx4j4mkDQNAbCdbXKM5dsk8y0lS5SHH+TZULQWsgZ9JvyBp9PwgCzqKZFDxQTuaIaWis6VguGog+Kz/pmLr0XohBpaJ1oPZgwmd3jp6F1pyG553Nivdvriolo9PiyZhXSN2RWsp37Oaxp8t4eoiH4Dw7r1xvwaqH3g4BTwg0GvhPa/Le4UOumOKxvbVRtdxHRUVFRUXlDKN61xam3lpM8bC+TL21mOpdW07q+MajnyQNViPtDV1rj6KZeN7ZIls6xl+TzG5p8LAxUgZ27woEnSH1micSorF8PrWPT6Bp4wIs3/kBMX9H0nNp7CyZ/iyfc/CwMax4sYKKXQdZ8WLFKWXLJ40YwoHVS3n8wdncuSXG7sMRwlGRea8FePadMOU3WAjMtVNWZOLBXQEml4d5dPbP1SBY5TOhZoS/4aTasQPwtbfg7DYJxncTvTV7aN+/lnBzPfrMPMRwkNrFE0Ak6fNRTwtekgd5nmCEzG6P6exOEDToc79N1N1M2g/HS+XIzXWJ6tNblyCKIjp7ltRnHA5IPngi5IyXSqKPPXtvogDX2Nm07FyG4/9NThCZiPctCwZlX3CqndRI23EEvZGGl+Yi6I0IehOZw+5WqFbn9y6gdOULPDdGI+9kHvdIpTzazq2oXKtAsyDy0J6gogx6b20UuwF2H47I5dJquY+KioqKisqZRSrxUUheSpys/9ae4UzaXhb3+AVp7RELeBVjde+97TnuVVf9CL/PA4I2oerOtXUJ5m//AN9Hf03QS9HaMpNW3NkznEnLt59Zuhh3q+uU/YI/jXhg+9MXVvFBbQMOk4b11yur6J4fZ+aOKp0aBKt8ZtRAWCVpT8vUW4sV3rsA+qw82vatUdglBetraKpcRMbgO2jZsYy2fWvw/us1xUTbVLEQS7QjqUCUzaglcORgQnAbaTmG/aqxivdq27eGxg0liCEfOkcvxHCItB+Mx/12JYjgvPYBrIWDFH7CqQLYcHO9HKy27FhGuLlO3hUt+N8N8ntpzWnSTqzBnFAC3bTpMTRWB9lj5yg+q6vqKVr3vEDU3YTGaOGO665h5V/KFb0tc3YEsBikUmhZ/XCTnyffDDHsfJ1CJfqKvpdz3+5jskn8g/epJvEqKioqKipnEqnER5OVEicLmsuefBRRqyWruyjo1iXEwkGyht8jHxvP4ra8tgLX1lKIhtEYzVxzzbik475auQhRFMmZOFfSfel0+dA5ckEUCXzyDrbLRsmP67PysBQOxltTnRA4u7eXoddosQ9PXr6d/0D5SfcSnwwTRwzhtknFuDvc9Bo4JmmCpc7lPq3vqfL1Rg2EVYDEHcSGo4clJcJuGVPzBf1wv12p6J3tnmEVQ37cByqxXzVWMaHaLhuF5+3N3LQxxOrx3ayA1vtxBzV4Oi2ZNJY0RBF01nQibcfxf7RfIeyQMfAWzL0vo3H9Iwg6PZlX/wRLn/507F+HxuKgff9arIWD0FozaNu3Bv/H+1N6C8d3V+Ovb9m5jLQfXUfL9j9S+/gE0OrRmCyKfuemykWApILdtm8NgdqDideieA6NG0oU6tZrq/5AjsOk2AhoC0py/z13Motf8lH8kg9vCOwmDdeNHs1js7q81FRUVFRUVFTOHKp3beEvq56hof5wSvXlniQLmmN6SWMlwQWjfD5aawZiNCKvRTKHTkVrzaB5WxnndvYO76pawr591Yk2S2Nn07j+EYx5hQhanbwWEqMRuZrPW1OttKGsKiXa3ojB6iCws4zG5ga5YvCJBb/E8Snl2yfTS3yq9MnLZm+tJyHBoraOqZwMaiCsknQHUbOxBJ3dSfrAW7s8gg3mhN5Z6Mqw6p35hF11igm1bd8a3G9XIgb9NER0FK8N4A3FsNusuIN6IIwuLQfzhf3wHHwV+2Wj6Ni/DsFgJtxcl0IgKwCAa/NidG/8GY3FAUDYVcfR5XcT9bTgOfiqIlPb3Vu4qXIRtr4j5ZtK3P6p7Y1V5Ez6jaxg3fOGlD12No3l8+l4cx0aoxUxlPxaiCGf4rjAhQMJvP0yt2+C58dJGWBviKQ7md4QNLyx9bT9blVUVFRUVFROH90TB/YMJ8GoSHrRLPTeZXIVXVeLWB06k43qXVsUQWEyx45Ie0OKNY+/K7mQmUfM24a1cBBiNKIIPtOLZtHw0tykQWrcCSRBWNRgAcDZU026aCaN5Y9gNpuZek+ip3GysXqWb5+MX/CpMPOO27jz6TKeG6O2jqmcOqpYlopiZzI+odquuJamyoVorRmcc/sfyJ08HzHoSykwpc/Kw1TQF8FglidU34f78NVUkzNhHgX/u4Gc6x/Cb8hA1BrxiiZyrvsdBQ9sIHPENHw11dj6jsL7z11orOnkTJyX9L3a9q1BY00nc/g0Ch4oJ+uaGQg6Pdo0Jxqrg6xrZqDPyid7bKJoV8v2P9K8rQxrn/54a6qpfXwCzdvKcAz4Mf7/HFAIg53ohtR7TiWxoA99ZoprkZkv/+yt2Y34zno6QiIaAW7d4MdU4iat00u4O3troxRkp52OX6mKioqKiorKaSaeOGDAVPIfKMcX08rKynHdkdbXV9H6+oud65QNZI37JUuXPikLSlXv2oLWZE1YP+gcuSnWV/mce9cf6T2ngswR09A787s9pww+Bb1JMYa3Zg/HVkwHoHFDCa2vr5KFPV2ViyAaQUzpyhHANHyG4twhuciqa+sSHP0mK877ZPyCT4WJI4bw4H0zuG+3DVOJh/t221SlaJWTRs0IqyTdmUzvP4WO/euk/tmWOjRGCxZHJroL+yUKTHX2CLfvW4MY7sqSJvWsG/OAVDqcxL+3aeMCxGiEnEm/UdxUur9XstLseOlQzkTp8XBLCouAcBDEGJYL/x8ZQ6d2Zqs307x1iSz0FUeflZd8x7MzyNVn5WG+INm1WIit7yhACoJ1bzzNuhuM8m7lXRV+/jzBTCQmMmW9nzWTunqEb6sIM/dnahm0ioqKiorKmUjPkubum+bWwkEEjx7CfaBSzuLGPX/jpcIAS5c+ieXy0biqShXlyLGgL6Ent2cFm2vrEtKvvkUOPjMG3S6fW7C+BrvdTlvVEtKLZhFxu2h7Y1XCeB3716F15GA4tw/BYx+kFMXSZ+XLZc4rlj+paJ8bfPVgDuxdQd3RT0jLykUbDSvKt0/WL/hUmThiiBr4qnwu1EBYJaWXsN4pGbfHy3sCooj49mbsV14rB8iCwYwYidK89UkQRTmLa+rdN7VnXYrdx1jQCwgJN5W4QJY+M18uR+6uTK21ZoIYo2HtPPRZeSknda0jp1updx2CwULWyHuxFg7i2LP3Ko5x9Juc8oYUf77t9RexFA7udi0sGHpdgPdfr2HufRmR/atYV6xR9AE/Wyz5ApcVmQhHRSau89MRFOmTl8Pcn6kiWCoqKioqKmcqPRMH3TfNvTV78P/fP8iZOE+hvgxg6dOf2vrDPLN0Mbo+Q/B/vJ9oRyNNGxcQC3jRZ+WTOfwnAF3taDoDtkuH4/94Px1vrkNrtCBGYzRvXYI2LQcxHCLkqqV9xb1SwsJg4dJL+nLog0M0bysj6m0jZ9KvE1q8XFWl5E17lmMr7iV77Byi3taETf3mqlLSu9lANriOY7qxRG6fq+4MdOMl0/Fy8Z5+wSoqZzpqIKzCzbfdIxuudxdKsHznB7S9/mJC9rfj7xsgGkbryMFaOJiOf2xE58gl6m3DfGE/2eZIn5k8qyoYLSl3H+P/j99UvDXVUhCcJQXlrdUrqf/j7QBkF8+Rdzzjvb3xHdOmTb8ne9wvFGrOAGIsSubwaTRVLCRjyJ2yaISj32RcW5bgHCMFvlprBrFwULJlCvnR2rMQEOjYvw7foddx9J+CpXAw7rc3IwZ9CEYLYihI6NgHiOEgjRtKIOhlQIFdca3jvsC3b/ITFSGms+B9dxvuDlXlUEVFRUVF5UymZ+Kg+6Z5+18Tq+CyimbSsnMZWmsGuvReuD0daN5/TV5rxNdV5gv7yesRa+EgKeNbVUrmiGlAl7CVgEDBAxsQtDpadizDc3C7wlby/cpFWPuOImPgLQoHjTjGvEKiHU0AhFvqZAEtUAbgWdfMUAiM6tJ7nVANO5n7iIrK2YAaCKsovIRr6w9LWd6wH29NNdnjf5WoEL1jGZkjptFcVUrM70ZjkJQO40Gp8dw+UhY36Evq/Wu7eEjS8mprn/4Yz7uI5qpSWbo/e/yvlDYCkRBao1UWskrmE+wcPQvX5sU0bpiPGPSjS+9FxpA7JaGsioXEQn6IxSTP4k6shYMIuWrlYwS9CY3JivXyIrzv70LQaMka8/OEwDqeUfbW7KFl5zLsRi0d7UHSzHpCMSGpZZTDCI8NM9HLJlD8ktILUEVFRUVFReXMpGfiQGvNQBsNE9z1FOHm/6YQE62TM6xtb/w5uRDnhvmYe1/Wtd7ZspiMwXfK40jCVmZMBoMciAdqD5I9dnaCQnTLjmVkDLzlBC1eeQSOHEQwdCUlrIWD5AC8aWOJUqW6M3HQ83N90WJYJ0P5jt2UrnxBtpmceYdaYafy2VADYRWgKxiOq0cb8wqpXTwh+aTeUi/vdDZumK/o2RU0Wtre+LOcJY152+TSZkFnJHPkvdguGYr3vIu67T4asX+/GPdbmwjUHsT8nR/gfnszORPmJgS4zdvKFD05qcqvo54WAHTpvRTWSdnFc2jeVoYYDiYE4573XiVz+DRslwyVxxKjEdxvVSSWFxXPoWlD182iddefyDEEWV2so77DyEN72vlPWGT8Wh8zfmjgt4OMco/w06PNTLlUTzgq4g2d7t+kioqKioqKyhdB98RBvAx4xv2/ZOz4G5k8YUjSwFNjtJI+8FashYNwbV6comXML69vpIq0gCIYba4qxX7lWLzvbMa9vQxGzlCsf7q3iwnmcGSbAAAgAElEQVQ6A96aPTj6JeqsNFUuJOZtp2XnsqRJCff2Mq4ZVSz3/+ac9y2MQoz2fWto3vok+qw8HP0mo7VmfOFiWJ+V8h27eezpMp4bo2FAgY29tR7ufLoMQA2GVT4VNRBWkekpAtG93zdOd5XC+OTdfVK3Fg6SemEen0DB/Wvx1uyh7Y0/Ewn6QNDIWdjuu48tO5fJ4lzmC/rhee/VlH3EkfYGxS5nqh1PXXov2Vsv3qNjLRzUOcZxQMB57QOKYFxjtCqyxPGxxHAgRU+zj+ZNjxIJeLDpRVZfZ+G4R+S31UGeLTYr/JIXvBHi2xkCJUNNTLlUD0jZYavh8/zGVFRUVFRUVL5MUpUB33zbPTz11OOkF81SVLJljpgub8bHlaHja5b4Ggkg6u8AUURjsmI45wKpfzjoQ5+VR/rAW7H06S+1ZwV8+MvnI+ik7HDU25rQxhYX1bIUDu7SWcnKx9pnAL5//w3zBf3wf7SfSHuj1AIW9pN73vkJvb3Vu7bw1FOPk3XNDMXY2miYGff/8ku42krKd+zmDy+s4oPaBjnzW7ryBZ4bo9RjeW5MhPtWvqAGwiqfihoIqwDSZNfTDN7Rb3KCqmF3AQWpVMeUwpvOBCgDYzEcwrthHo3BGFYD+AQrsWgEoiGOrZiOxuLA//F+bJeNwn2gkrZ9a/B/vF/yzutUadY5chW7nGk/ui5B1CqupBi3Tor36FgLB8m+eTp7VkIwbr6gH02VCxX+w40bH0MwmlP2OmeN+yXu7WV4W/7LgAItVyz38myxWTEhr5lkZsJaH+EY9LIJhKMie2uj3L7Jj8Om7CFWUVFRUVFR+WLo7gGcc963uPm2e05bb2t8nBXLn6TBdRxBbwSNVpHZjfo75JaxZKrOrqpSzBf0w1tTTSzgpfcvKuXxA0cOglaPxmSVj3dtXYKg0SU6dIyeReP6h0EQEMMBWWfFWjgIMRbD896rZBfPUWSC49eietcWnlm6GLfbDYiyk0fPseMq2F9Wb3CqzG9tSyCpHsuH9U1fynl9owkGED/56Ks+i8+FGgirsLS0hG2vVgBwbMV0uZRY7pvt3C0UDBbsV16LpU9/qY+kchFiKJAQLLuqShFDAXn8YH0NWksaTqFDYRd006YwjSEjGaPuQ2d34tqyhLCrjlg4iKl33wQRiKbKRcRCQbTWDBwDfiyVEbUdB50BV1Up0Y5Gufw6vvsKneXcrjqOLr+baNCHGPRhvvLaLjuCqlKshYPxvv8ags4kC2QJBjOCRovtyrFJe5rFSFgWjdBt/DV7a6MccsUYUKBVXN8BBVo6giAgcneln8NtIuenC3hCAgtmT/tyfskqKioqKirfYOIewPaRM2T146VLnwSSB3OnEjT3zBbHx6itP4zObEMM+hAjYVxbnyTmdye0XTk7N+6dRTNpLJ9P4MjBBG0SRJHG9Y8gdpZPRzpcKS0jc28sSVCwDnzyDsbzvicrVmtMVoz5l/DnF5azc9tGDv7zPWkNZLSk9hiOhGDA1BNev9NNqszvpJe1SfVY+uRlf+Hn9E1HYzFjv6Lw0194BqMGwmc5p7q7GT+uof4wgtGMGPKjz8rHfGE/Wvc8jxiLorM78dVUY+rdl+DRD7BdNgr/R5KMv2AwY71oMP7DB7AWDpZLjPVZeVJQ+c+dit4Wm15kzThlpnT1OJhQoaXjb69w7l1/xDlG8gOO3wh6ikBkj51N4/qHFYGq7fIiPO9tw1k0UwpOw4Gk5c3xz+Z+ezMA7gOVdOyXPocY8uM79DrRgAeNwaSwPmh4aS7p/adgcBZ0fcbMPGLeNnl8Y14hjYEYN28McX56coGs3g4BT0hUnJdWqwyYVVRUVFRUVL4YerZ/JVM/jnOyQXMqBg8bw6H33+XV1layegiHipHkbVfx3l8x5Me1sYRowIcuvRf67G8R+u/HCDq9IpvbWD4/uWWkLTNBwTqeQIiFAwox0qbKRTR422hqbVWsgRo3JB9bn5V3wuv3RfBhfRMDCmyKxwYUaGnzR7lzS4znxkTkRMudW2I8eN9tX/g5qZz9aL7qE1A5deITNQOmkv9AOQyYylNPPc6PrxtK8bC+TL21mOpdW1Ie58//ARprOjkT5lHwwAYyR0zDc3A70Q4XLTuW0vDy7zB/5wdkj3uQWNCL9587CTfXgdYAsRied6uI+tx4DkoiUwUPlJM5fBqeg68S9bZRu3ii1P878FY6OtzJM6Xt7YSb6wHkib/7jaA70i5kEI3Jiu3yIrQWB553qxD0RlybnyDmbZMz1IEjBxGjEQJHDtJcVYqpd198NdXkTJhLwf9uIGfiPLSOHOxXjUVjTSca8CAgysF3vKxal96LYH2N4jzEaBiNxYFgtHBkYTHHVkzHZLURjEnB7pT1fnYfjhCOiuw+HOH2jX4iMVh3vYVHhpi4yKnhcJtImj7CI08tO83fChUVFRUVFZWeNB79JOm6ojGJ+nH3oDm+HrB3Bn0nQ/WuLWx7tQJn59rC9+G+zgo2F4LelLC+iAeZwfoaLI5MnM5ccm8s4bx7niHc8G+puq7TKSN+XoLOgGvrEsW6x7V1CSAkfNZI23EEgzlhrZM9dnbSx+1XjqWpcmHCmsrRb/IJr9/ppnzHbhwmDXtro4rH99ZG+V5+Dg/eN4P7dtswlXi4b7eNB++bofYHq3wm1IzwWUyy3c30IklZOf+B8pQ7mPHjUmVd4/ZITZWLcL+3g4jbhaAzEIuGEUw2NAYTztGziLhdtOxcjq3vqM5saR0ao5VYwItgMGO/aiwZA28BILL/BfbWehIypXabBb8xC+iyB+h+I0jmNazLOBfvoWpFFtv7r9fQWBySNVI0QsNLcxH0RjRGGxlD7qB9f6K/n1R69AigQYxJvco9b5KO/lNoLC9BY7LgHD2LqLuJ8L6VdPjasFmMcNlINJ+8SYfXzbkZAkXf1bPuX2FFCXQgItLkg/qOWBIhLQ9rt25n9IB+p/nboaKioiLREgGb3UyGxQOolSgq30x6egCDtK5Ipn7cePQT8pMEzSdjGVS9awulT8wnFvDRsmOZFAj/+29yT3DbvjWJFpOdrVpNlYuwGfU01B9Gv2MZ4ZZ6EEViSUqVY/4Oskbfr6jMS7/6Fpq3LlG8Llhfg8PZi3bX8eSJhpA/4fG4kGk8kyzojWhMNsWY9gwnU28tTqhMPF392PHe4Hu/r+WuCr9iDRXP/E4cMUQNfFVOCTUQPotpOKoUt4IuZeX4bl6yspX4BJ8q6xq3R8oeO5vG8vn4P/4buvReRIM+tCbJwzfqbaV9718QQ75upcN/QmO0EAv60Focchlyev8piBcMZsr6l3v0CEdwh3RYv3OJ1HNcsRAxGqGxvART70sTbhDNVaWYv/MDvB/uI2fCPMXj1ouH0vH3DWiMFpzXPqAoPwoePUTYVZeihyaAxpKOGIiARsexFdNlZWpHv8lSmbUgSkGwtxndvqWsK9YwoMDOQ3uCPPvOq6yeaGZAgZ29tVF+XO7n7qsMzB9qkt9n9+EIE9b6pNcnEdL66bJn1EBYRUXlcxMPeHtiAwoLIqSbtBiIJh6oovINoKcHcFwkavr0+xNeezJBM8CObRtZsbyUhqOH0ZlsRPwetNZ0nON+qSg/tvUdKY8ZTxQ0bihBDPrQmKREgv+j/cS8bXR4QWtNJ3PENIx5hRxbMV0+j+7npXPkorM7OfeuP8qPxX2Ce/YY20wGBKMl6WdLKX5qtBAL+0nrdwPp/afIAXvIVUvg4KsIOj3mAVMVJeSH3n+X6jeqT6q0fP2O3SxYuY76+jry8vL51R03MGnEkG69wQYuztYyoyrAIVcMh0nLo7N/rgbAKp8LNRA+S6netQWNIbmacdzeCKRgr7b+MGOHXorObCMS8KAz2Wja9JicfT2hPVLYT8H/bpAn0UibtJP43+d/JqsxB+traK1eiaA3JEjsu9/aRMebL6PPzKMjoGNChYGO9nYcWdnoBt5CjjWLxvUP4z20B9CQe/3vaKpchP8/72D+9hVyP7DGZMVaODipgXxW0UxadixD0GjILp6TqG64oUQucU6WYc4cPo2GVx5CY7AmnH8s4JN3STue/wnriruEGjZ+EGH1RGVg+5eJZmZUBZjfZUUsi2V1BMWk5eEf1B4/Dd8IFRWVbzLxIDge8PZEcmpTg2CVby7JPIB72gXF+bSguXu2My0rF3/AT8a1cyjo1rfrTFFxFw+AoTPj+ubLIAjkz3wJkILY5m1lEPSQ1W2M9Ktvpnnn8gSnjKi/A9eWxTjHKJMAtouH4NryBLGABzEcRDCY6GhpRzDaEhINTZWL0KblJH1cDIewX3mtfN7xirrmTY9itdowDU/su96+6VGyxv3yM/VjgxQEz37qRawjZ8qB8+ynJHGv7r3BUy7VM+VSPeGoiKnEowbBKp8bNRA+S3lm6WJEjU4xIbbtW4P77c2IIR/Hnr1XNj3X2jJBq5NVj9v2rcFz8FXsV16La8sSnGNmKbKr3e2R9Fn5XT0kxXNoXP8wR5fdRdTbgjGvULJY2rqEWMCTQmL/EXrPqSBw5CANL83FMfV50rVdXzsxGkGMhMidPJ/G9Y8Q9bai0ZuIedsI1P4T+1VjFTuQ0Y7GFFnsOhBJUe7jI33kvcmVn2MxGtbOA40OQaOlYe08ORvsHD0L1+bFaOxZBOtraG9WCjWkUog+5IopHttbG+XbGQJNPjGpkFaaSf0zVFFROXW6B8E50WPovakD3rA1/0s8MxWVM4tUHsDJXgfJg+ZkQlqerUuIelsRtDqi3takZcbyWqUbwfoadI5cxEiIYyvuJdxSh2CwoAGiIWUZtGR9FKV5WxmN6x/uDG7NmHr3JXT837KSdLw1TZ95Hr5//42cSb9JKL12v1UpZ6IFowXbxUPIHDGNhrW/7hIk1RslC6hYBP9H+zE4C2RHDmNeIdGAl46AF0eyykS/J+nnT1VavmDlOqwjle1rgT6D+OWiJYiiyEVPe3hkiIkpl+oBVRVa5fShrsDPUtxuSXY/6m2VJz9J+GquMqPZKdWfe/3v5AnG//F+ssfOIeptxRMJdfV+GM3YrxyLGItydPndnY9ZOPL7seid+RjzLgEEot4WBL2Ztn1rMDgLEMMhxHAwZelx4MhBGstLEPTGlBno+Gt7msI3V5XKk2/cTiB5SY8FRDH5+Jn5kofw0UOysbzWno0Yi5Iz7sGkXn7NVaU4BvyYqLeVjCF30lxVSprDwd7akBzIXuTUJA1sLXr47h/c/HaQkbw0DXdV+HlosJHbNga4qdzfWUrdWR6+MUy7X83SqKiofD7OcZpIN/nRe6OEw5akr9HrfV/yWamonL10D4Ybj34iC2Ul02dxjp5Fy85lWAsH0b5/bcoqtJ7lyvF1GqBo62qrWoLVak0sg7Y70aefw7lTpTJob80eWl9/EeeYn3cpPW96DM/B7Yghf2KComgmTRsXIEb8aIxWRAHyZ/wFoTNBkTv5ETzvv0bztqcQ9CaFOnXcfslaOEhRJp7sc+rMtpMqLa+vr1P0ZHtrdmP+1wZWX29gQIGZvbVRbt3oJxITyUvTqKrQKqcNNRA+SxHDkuy+oNXR+vqLCEI6WdckTsyuzYuJ9tiZjPcGH33mHrLH/QJT777UL7urc5dwU8Lk56oqRZdxLr6P95Mz6deKjCqiSPa4X+CqKk3Zc9Lw0lxJaVogoaQnnoGO96H0FLSKl19bCwfJYg4Jmd2KhYiREGk/nJgk67sQ47nfAyBQe5CcCXMx9e7LsWfvxTn6/m7/n5XUZkCyg5J2QFt3P8uU9X65z3n893QJge2U9X7SjQI3XqJn9o4geg0sHGGil02gMFvD+O/pGL/WjzsEaQ4H7rCWLGdXP7GKioqKiorKV08qCyVf63GFPou3Zg/tf11LuLmOY8/eS9hVR9aYWUnXKraLh0jVbe2NUrZVZ0hIVsSFT10bS/BtegyMViLtDegcuUT9HdivvFZ+b2vhIEKu2q7sbqeYVcbgO3BtXpw0QRELeil4YINURXigktrFE9Bn5WMq6Ivv4zeJBTxJW83ibWghVy2edzYTC/pwZPXCX7mQjLFda0b39jJGjryW6u1lJ+zHzrB4MHiPAZDpzFasIcOvP8O68XpF69mL480Uv+Sj4JxePHjfLWpZtMppQQ2Ez1Iczm67jbEoEXfykuGotwV9Vr5igtFn5dG2bw3R9q5joh1NpPefgvutisQ+284dxOzxv0rq62vMKyRj4K0JQW5T5SLsVxWT3n8KdaU3Yv/+ODzvbpOCc08LuvReOAb8GK01QypTTmHcHrdXCtbXoHXkIMaiclmQ1pYpZWy3LpFEuYJeOeurz8zH1ncUnoPbaX19lUIcLNX/u79vpO049iulHWFr4SC01gxcWx5nQkWMjrY2cjMsuAMhbtng55hb5NsZAotGGPmwOcbT/wjREQSHEf7VFGXua2FKhpq4rlDHgr1hCuZUStfxyEGir/3h9HwpVFRUVFRUVD4Tn6ZqnMp3OLTpUXlN5a3Zk1DJ1lS5iHDLUdIH3iorOescuQhaHZkjppE5YhpiNELt4ok4vl9M9MA6GtfOlbRT+t2CtXBIZ+mxD43FQXY37ZKWrYsJHHyVQO/L5Me8/3oNYiK69F6dYql6Wnb+CW1adtIEhT4rH9+H+yRLyU7P4HhrncZoIWfSb2hYOy/FeqyO2EE32eO7qg8DVUvwb3+SxlaXooT8oksu58XO0nLned/ilp/cT99BY+RWjpxoLXqivPTqO3h8ASLd1pAd7R0MKLAr3n9AgRZvCA5UvIy7w/0FfjNUvkmcEYHwe++9x+OPP86qVau+6lM5a5h6z/089dTjpBfNIuppSakCiNZAxN0s2QkZLVgvGoT5gn64365UlO5obZkcWzFdzjR3x5hXSCzgTVH6HCRYXyNnTeM2SoJGh82owb1/LbEDayGmR595HpnDf0L7/rVEPS3EAm6atyxBMFowFVxKsO79bjuTl6Kt/QftzY3YjTraXn8Rz792E/N70VodiJEQuvRexII+BI1WDva7Z33jmHtfRmP5fARdV2l2d3umVFZNgt6I5/1d+D56E+ulw/DVVJMx+C4sffrTvngCWp2OXnaBBq/Irlstkgr0P8Os/meY8hssiizx5bkaplyqZ/fhCI6sbMU1rGtqRB9qOK3fjy8CwduBPuT/qk/jS0edn1S+juj1PrmE+kTzz9nyd3+2nKfKmUGqbC90lUSnslCK+D24O7Od7X9NtGbMHjubxg3zyZkwj3Nu/4NcAp0x+A55nGB9DVqzHdM/X2bNjfH1goebKp7GC2itWWhM1oTEROboB3BtLJEtIsWY1FqltaYnin0GfQkJCldVKRkDb02wlPR/vB+tOU2uLEy1LtKaLAkiYOlFs2DvCv7yymuKa9V30BiWXXsd53RWvW2vLOeRn1zDf2qPkut0YBBE6po6SDNqMF0+Cb2zt7xxYDOQtPXMavh8v3cVlZ585YHwM888Q0VFBWZzouWDSmq6964IOiNi0IerqhRnt13Jxo2PoTGaye5WstJUsZBY0A/REDGdkYZXHoJICI01HeeYn9OyY1nSyU9jknpVot5W2vevJdxcj9aaAVoDDS/NlbK7/aeQOXyadAPQxUuI7bJVUuNrz5A5fBqOftcT2b+K9uZGbEaBaMGlhBsPkz3+VxjzCmnftxrTP19hzUSTfPyU9S/T7teiMdkUk31T5SKadywja8Q0Gjf9HiJBheCVXFId9pP2PzfIiohpP7pOvkF0/3/3m0jmyHvR2Z00VS6i42/rseljNFcuIrB9MQIaIj43z0wyM3yVTxbNKnkjuUXSxLU+5r0WYMU7YXRDpiuube/zC9BecOYL2GjNFrT+b1aPoTo/qZzp9LRMCqNN2QscF8qK/6v31sGnzD1ny9/92XKeKmcGqbK93VWNU1ko5eadz8233cOfX1hOuDmFNWPQL+mvtDegTctGDIfQWjMQo5HO7GslVtHHmhstivXC6uII49Y9iVewEQsmT0BEAz6yxsxCZ3fiqiol5mtPaO+yXjwU94EKoh2uLvGrThEta+EgXJsfT2iZi48P4Og3OaG0u61qCdFA8sq92qOHFY8pVez9bHilgucfncdzRVDfYWTe7nZWjjMzoMAmrRErKvB+eyhWArQjEhUFbt/k5/lxXa1nt2/y47Aps8QqKp+XrzwQLigooKysjDlz5nzVp3LWEVc/rN61hSWLH8ZaOFhhqI4YIXvsPOVOZfEcmreVce7UpXLAR0zEee3PMfXui+P/JU5+rqpSjPmX0LihRPLp7REwpl99ixQwViwkFvKTZtaxZoIyGFw9DiZUaGnd9SeyDUHZi3dvbZQp5X8ncOl18nkKH1ezZqIpIZgsfslHwGCWlRm777wGjx5Co9PhHPcLRZDsqnwcXXovBL0F99uViEG/fFPQ2rM6laib0JjT5B4bXXovMgbdLme57X1HYDqo9ECeUu5n6hVS/0p30axUStIdISj7ewi/YCbTmoUYjUglTTvLmL9oLl7rt76kb82pEzOb8Wu+mIzLmXprU+cnlTOJlkjyx89xmsi16vAF7WDN59NskvTeOsLWfMLWfEIkWi1154v8uz+dfFHneabOTSqfj1TZ3u6qxieyUIqvv6beWpw0WHY4exEIR8iaPF8uPZb9gq0OcibMo3Ht3KTrBU8wisZuQGvISS5Eld6L1t3PIWh1GHp9F//Hf1MEp96aPXhrqsmZ+GtFEDts8HC27ajC3PsyNEalEJc+Kw8xEpYfi69/4mKquXnnY9QKRFOIgGmMFqp3bWHwsDFJVezLHvs9zxVJ67lL/ujh+XHmhA2ACeuqKL9BCo4f2hPk6b+HuLvSz+E2kfPTBfxRHY/MnPY5f/MqKkq+8kB41KhR1NfXf+bXazQa7PaTvzVptdpTOu7L5lTOc+z4G/n3h/9i87ZKnNd2+b81vDQ3ee9re4McSMYtjuKvk0ucd3SWOBvMiCE/EZ0RQatL2HV0jp5F08YFxII+dI5cYkEvbo9PYTMEncFgeztWvcjq65Q7oGsmmpi4tRo6bZt62hTFj/eGBXKumaFQLowLaHne30XOxF8n9ewzX9gPz8HtCd541kuGdVkzbV1C5rC7cW1ezLlTl8oKitAZmE/qkeXt5hc892ojN77iw24UEEWSyvw7jNAWgJ+XLOTPy0upq/sPOfnf5p5Zv+Kcy8fz1hHxpH7nXwWCICKKxi9k7Gszv5BhPzcnMz+d6twEX+/56cvmbDhHOPnzbAxEMOvhgvMzkj5vt0vlhwZt8mjZotMAILQfBrMFkz0NXySGzxuipi71UuCL/Ls/nXxR53mmzk0qn494trd7lZvOkYs9wym/pnvlXW39YXk9VPrEfA69/y7TZ849YbD8zNLFXZolWflkjbxXUnjuLEl2ZGWzt9aTUP5rM+oQLh6K+61NNFUsJLt4DhG3i/Z9a4i0HUfryMF22Sjcb28lUHswwZGjff9a+T2gq3x5384yYkEfzdvKiAW8iipCqWVus6I6Tspgh0jLzGbFixUUD+tL5uj7E7yGm6tKsV1xrSKb3lPF/sMjjQz4sbSuS5UwcAdF+VrMHyrNZ398KwrEMNiyeeSO21SBLJXTzlceCJ8ssVgMt/vkm+TtdvspHfdlc6rnOfXeOXy3z8UKzzutKXnfsD4rT/45blvU83ViNAyA1uIgfdR9WAsHcWRhcUoFwqzRs2jftwaiEawpejvSHA462tqSToDtzY04On9OdXNwZGUrlKQB2t74M4iAKBJxuxLOLdxSDx/vJzuJsX3jhhI63nxZCuDDQQB0jlza9q3B//F+ObMeaW5MKtrQ3S/YpBN4ZqyyhCcu83/bRj+Gzo/8zCP3c/U11zPt+Y3ysbZI9Kz4btq+0L+hvE9/yRnOqc5N8PWfn75MzoZzhJM/T39ECoL9fj+51sRbt9sd7vxf4nMWoxs/YCCKvrN82KvxcrjFzMeHvWSeYCXwxf7dnz6+uPM8++cmlURuvu0eyp58lKhWr6xyq1zE0tISps+cC0jB8KH33+XV1lac3YK/VysXAciv+8uqZ6QN7m5iUYtLHgSg4H83IGh1eGv2KERKdf1u4aaKp1ldHOmyVKyIobliIp6aasRwkMyR99Lw8m/RGK1KN48tS0CMkTNxHhG3SxHApirXbnAdJ63fDXhrqtGl98Jy0UBFFaEu4xzCrjqFGKlWjHL3dKkiKue8b4HdSczbJiVLWqTj0gfeiqVPf+oWv5zyevfpncve2o6EKro4e2ujXOTUKI757SAjj+71cPz1LUnHXL9jNwtWrqO+vo68vHx+dccNTFIDZZWT5KwLhFVS09MofmlpCdsqFyb0CMcn5LgXnNaRI5dDJ/PUjZcYx/uEE8QT0nJo3/sXuZy6fd9qpqxXlhLftClCRwCsxuQToNWgofX1Vfg/2k+4uZEp6wXl8RUxdFffAnQpF7bueT6hTFvQaOWsdjzoT93D46P3LyT15sCRg5KaddCXkD32bpiXPLA3wu7DEUreCPL8eGXG+PlxUim33SAQjYm8dF038azydSwDpv1s3un9AqioqHztSRYEfxq+oB2L8cwPZlVODlXI79QZPGwMK5Y/iWl4D9vJsbOpWv8Q+/ZV09HcQM5538LlasA5fm7C617dKAXMg4eNYez4G+WNmOpdW7j5hhEACEYLbfvWkDHwliT+whqaohaKX2rBGwKrAfzGTDKcvXEWzZREPjVaNHpjopvHGMkeU24p02jlMmbBYEouAGq00PHmywgmO0TDdOxfhy69F1mj70dnd9JWtYTRYyZy4MDfaDz6Cc50h0JJO5791qX3InPENMX4gSMHU3oEA8z62T3cWbKQ50ZH+EV/Q0L/74/LA9x5hXJu21sbpU9edtLx1u/YzeynXsQ6cqYsdjb7KalaUA2Gv97EYjF+97vf8eGHH2IwGJg/fz69e/eWn6+oqGDlypVoNOF9W5cAACAASURBVBomTZrETTfddMLx1ED4a8z0mXM5Wv8JBzt7YnXpvcgYcqcssBBy1eJ+twqN3kTE3ST1zkbD5F7/UGL2tHw+sYCXph6BtauqFGJRssb8nKi3lf8+/zPCzfV4zGkUv9SBNyRiNYA3qkcUw3hiMaaUBzqFsLqC3FDG+UQOviqP3b5vNePWrccTiJKWno7+6ruwFkqTm6TobEpapt28rQxLn/4Kj+KWHUtTeByb5Q0BY16hpL5tMCVkjwNXXpcQ2N++0c/Nl+qZURWgpil5mY8vDHqNyPobEkvBJ7z8ihoIq6ioqKicEqqQ3+eno7kBR49N8ojbhaA3Yxo+A0fnOieaos0sGvAxduil5Oadz9R7ZvI//Ycp1Kgd3ZIJIAlSZY2+n+aqUiyFg/HWVGO9ZERnP+9MRamxY8CPEcN+Wnb+KaVrR9TTIq9hrIWDsPTpT+3iCWgMVrmkWmFneeW16DPPk5Id3fVUKhZCOMg1o8fLGe441bu2MPXWYtliavDVg9n7xq6E8Xt6BPdkUvEoAO77w3I+PNJAps3EhHUBOgIx0s1aLr+kL3+p+YBh53dlx+/cEuPB+25LOt6CleuwjlSWfzNyJgtWLlcD4a85O3fuJBQKsXbtWt59910ee+wxli5dKj+/cOFCNm/ejMViYcyYMYwZMwaHw5FyvDMiEM7Ly2PdunVf9Wmc9XT3xEvLyiXk9xIIhQGR3BtLFIGgs2gmDa88hMZgxjn6fsWE6P1gr6JcJu1H1yGG/RT874ZOwYf5iEE/gtGM/cqxdLz5stS/0i0rLE+uQhTj8Lux2p2SMFc0SoOnhYlbbbQ3N+HIyib67R8Qqdmj8ClOH3grgd6X4934KO4wZHcTmGqqXIgYChB1N9H+7J3yOLof/ZhI23FqH58gn5ulT39clY8nKGq7qkoRQ37a96+VM+OC3oQYSrSP0meeRyAiKkQb2gIimz+OUNsuYjcmLwW/yKlJGSR3+FOo3qiccajzk4qKypmGKuT3+empCu2t2UPLzuWIIR8tO5fJzhO6FAJRuvReCFo9DJjKkicfY3ogoFCj9tbsoX3/WmK+NtwHKkGjQ2d3yhv02eN/RcvOZQn9vFlFM2neVoY2LQfEGDpjCoEqSxrNrz4tV+wZ8y9BMFiwXjoM7/u7pCo3TwuC3oigN2FwFkj9wz2SCNnFc2jcUMLOnVVcdMnlgNQX3XD0MBqjBdsV15J/4xME62uo3l7G9OkPyK+Jt+LFy8FPxKTiUUwqHsX6ildZULKQ58abugW9HzBs0DDu2/0PPqxvok9eJnPvmcR1gwqhh72b4O2gvr42udhZfe0Za0f5dbR4EyJBdK4jX+p7HjhwgKuvvhqAyy+/nPfff1/xfJ8+fXC73eh0OkRRRBCEE453RgTCKp+fZJ547spF2L9fRMebLyfdTRQETUK5je2yUQmlwa6tS9DashC0OjIG3iL58m4oQaM343nvVXSOXNr3rcHSTbVamyaVs4hBDy07l2E8pw/O0bNwbXkCwWjFOPx+CjpvFJ7XX0xpExALeEjrd0NnP4ok3qUxpSHo/ej2Ps26cbpO+X0PN216GkFnwP6D8bjf2oTv0Ot0vPkyGpM1QVHbWji4swy7XiqL3roEjclKTCDhhhPet5INk7uyumv+GebBXQGeK5YyxA/tCXJTuZ/VE7syxndV+LnpUj1H3aHkZdVmnfx7+8uqZ2jo7C3qXoakoqKioqKSjC9LaPTL5ssUu5t6z0we+/3vcF47W24Ly5kwV5GZBXD0n5KQAY1nbZu3PimtF0bM4C+rnpHVqL01e2h7/UWlA8cWSTwrZ8JcYkFfZ5tXfXJR07bjaO1ZODur7RLcPDYvRoxGyJk4T5H11Zjs+GqqsVzwP/j+7x/k3liiOOdIR2PS9xNDPiJaB0889msM9nTSr7mfgm7HGZwFUtvZSOlzvrR+F2PH35hwTRsDETLTDZjNZsxm0ITNpPUoWigtW85zowVFpdxzYyL8tPrvvLO2DADtBQUpf28ajYbe5/fGn2RzoPf5vbH17fPZvwRfIhqNBm0s9ukvPJswmdBc+L3TPmxLSwtTp06Vf548eTKTJ08GwOPxYLN1CepqtVoikQg6nfR9uuCCC5g0aRJms5kRI0aQlpZ2wvdSA+GvCck88bLHzqZl57KUxuhiODH76f8oUVjKOXqWVALdSby/NhryIeiMxICYrx1vTbUy67p1iex111QpZZqjnhbsV4yWVQfjZvQtO5P7F+vSe5Ex8BYyBkr9wYEjB2ksn4/dpGP1ODHBoqn4JT8d/9gIkRCxkJ/cyfOJuF20Vq9MUDm0FA4m8laFpKAY9JE18l5clY/TuOn3aAQwR9rxhqRz6S6WVfJGUCH9H1c3HL/WhycEFzk13HSpnlXvhdEAU9b7e1gvBRgw6oakmxdLlz4JoAbDKioqKiqnjc8j5vdl8mWK3f1P/2GMGvYm2zaWIEaj5Ez6TUJmtmXnMjKHT0MMd/kCxwWitNYMWXzUmFdIXd1/SMvKJVhfQ/t+aW2j7Ot9gKYN82lc/xCCziDrmCRv3TIRdTdLSYtOF4v4Zr6gMyDGRIiFFJnreBub8dw+eN5/rbO0uuv5rKKZNG1ckEJENZ/M4dNo3FBC+jX3J70O8Tayurr/4Ha7U9q55fUykaYJ09ymw2LMJdjNzm39K5UcqT/O8FXSWmnu1UamXKpnQIGWD+pdhPKchNES0uSk/L2ZzWZm/+Jn/GLuIhjepdbt3VnGb0pm4zrBsV8lZrMZv//0ZoR7ndbRzhwyMzMpLy9P+pzNZsPr9co/x2IxOQj+4IMPqK6uZteuXVgsFmbPnk1VVRVFRUUp30sNhL8mpPLECzfX47z2gcTdxMqFCAZzYvazJbmwVLSjSf45LroAIIZ85M98ibo/3JRQ3uMcPUuehDV6M553qxCMFgznXIhgtMqeesa8wqTm7U0VC8kYcmfCuYghP+4wKSyWRLT2NGJBH7FwUL5xye8X8qHPzMdSOBjPwe2I4QCCVo8Y9KG1ZoDWgCBGyDaEuesqAxs/iHCkPabI6iaT/pfUDUNY9NLzfBDhseGShdKq90IUv+TDGwK7ScvV19zAtJ/NY+qtxQmbF4ycobAgUFFRUVFRUflimD5zLhddcjmLSx5MuvYJN9fh3l5G0eiJVL9RLfsCd9chAWldlHPet/D7PLi2LiHqdiWvcgv50dszIeijqWIhtstGJbRuNVUuwn5VMf6P9tO+bzXCx9VyC1jwuz8keOxDcnps7ANY+vRHDPkJNX2iyBR3fz4W8CoUprt/jniSI/l1qFd8zngQ/N085TpM0OsTrnHcq3zDKxUsnPcbKm60KKrnAHrZBPr0ziGMlkbtudR8lLqc1WCM0avvOO76qcCaZ5+irv4w2Xnnc9dP59CrbzF//Sj17/urxGCMEQqeuEz3ZJn4P6d1uLOCK6+8kt27dzN69GjeffddLrzwQvk5u92OyWTCaDSi1WrJzMyko6PjhOOpgfDXhJ69LtClmpzMGN2gAf1VYxOCz2TBcbC+Bp0jFzEaoW3fGtxvV0o9wgYzgl5SJxRD/pQ3kbbXX0Sfcz6xgJtYwEvLjmWIoojt4iF4D1UTrK/p8i/euYywq66z90bqp+lOsL4GjclKms2SwmIpB+Pw+2neVkbU10YsHCDtf67vLIOWSqvDLXXwMWQOnYq1cJCUZV7/sKSordFgN+m569Iwq/8Z5tliM/UdMW542YfDJHC4TSTtBD3BLl+MXKvAIVeMkjckS6a8NA29HRrKikxM3Z0ui2Sl2ryoO/rJqX4NVFRUVFRUVE6CwcPG8OcXlidf+5hssgjU3jd20bj+EWkDvZsOSeDIQdw7ypg+7X6eWPBLMkffT+uuP50w8xrYWUZ7qwv325sRgz6aNi4gFvSiMVqxXTGGjIG3EP7vh5gOxoU6pRawKev/RqDv9UkztlprRlLBT8XzehOIosLfOH3grfJaKJU7iD4zj8CRgzRVLMRqMrBp5UL27/8rx4/8h169v81PfnY/o8ZOAJTK9r5gVzVd6e+f4LkiFJV8zxabubvST0xr5Je/mSoFwbU6PG5/Sls3u06H2+9n+MARDB84Qvnkac64nk7i563y+RgxYgT79u3jxhtvRBRFFixYQGVlJT6fTy6hvummm9Dr9RQUFDBhwoQTjqcGwl8Tkpm6N1UuwtZ3JGI0gtaagVaMMHPuYwweNobiYX3J7j8Fg7Ogq3f2/7N35uFRlWf//5zZ16yTBNlRkZIqKr4tLz92BZQtEKTFoFhfwQoqVUqlSsCVoA3SoLSCVaxbiYAQSNhkX0tbq1jUoNKKEEQymSyTyezL+f1xMic5ZEJFQEDP57q8Lsk5c+aZOXM957mf+76/37T2iEF/C7N017oFRAMNsgiVGPKjT++A+apGA/b1C9Da0lop77Ggz+xC8MRnZIyZpRhbw4cbQWuQe28s3fqgtaZS+XY+bScvxvfZvhaBurOkAEGro87VusWSsX02EXcliCKGy7opep7r9hXTcHAz5q69cf91Oa51zyEYLIiRsCSWFQlQ7w6w5lOBpTlS+XPxR2Es+iaf4EQ9wXmr/Di9UpA8uaeexwcYZT/hQFhk4S3SuV8e/1r+flrbvDidBYGKioqKigqoQn5nS3OBUXuqg+DGIlKGNWVKPZsX8eCvpY1rqY3pIVLia5G1z9JwcDP1+1cg6I2AyIKCR9CZbYRrviJtyFSFLaV7XzGRupNozHb8G5/F467HZhDwxrQIZjsao5VY0IcYjRDzezj+4l2YQzUUj1daMxbfambshp3QmImGpqRDVdn8hIKf8eOuDUWk3/IA1uwBeMt3UbvrNdIGT8HYPlsKctf+DkFroPLtfHQpbUjukye5jJTNJ+qto3rTIlIH3U245ivWlpbhGPUwHcZJ38dz8wpINmoZnTu21e/78Jdf0XdCy0q+L2pFXnlhOsNv/z+O1Jhp8NSe1ttc5YeNRqPhqaeeUvztiiuukP8/Ly+PvLy8b3w99af2PSFeShtX8UtKz0IbCeB5v1SaqA0mTI0lKzu3rUdjNFO3rxj/4f2ygJS5a28i/3S2MEtPHXQ3YixK7Y5XW4hF2HuOxPvxNmLBhhblNq4NRYiRMMGKjxWK0PH+5ao188gYMwtnSYEUbHtrETRa7EYNFQvGkJyeifmKnzQG6hWgMaAxmuUxuPctI2f5O3hDMZLTM9H1m4g1exCBowfRJWcBEDr5OZm5s2UFR//h/cS8dXg+KMPecxSX9XlBDrAzcmZSs3UJlkg9h1x1cvnzqT7B8Z7gW1f4cAchyQj3/8QgB7+TSv38OENL3jV6JvbQ88f3QtxR4mfODgFHapOEe6LNi/9mQaCioqKioqJydiTS6AiUFRLYughno39wXAk5URuT/bphNBzcrBCjqiqbj76ttPlu6zGU5L63U7nyCTRGCxk5M4l6qtDt/SPLRuvo29HetIkeEUi/RZnEEAQN3lBi1wl3tZPmZjCS64WZWCiIoDfKG+xx1epwdYW00e9vQGtNbUqORMM0bPo9lXXVaCzJCDoDjhFNa7iq0kJsJgNWox7zqCbnkRNL78NxStaZwdP4/XO/P20g3LVzO57c5WTNpxEOuWJ0d2gY8yMdP+qSxc/G3IS31VeqqJw/1ED4e8TAm0bIAfHi5wvYtKkUMeyTs7feT7az4NnHEHQ6xFishTq0s6QAkPoXov56tEkZhF0V1GxZLKkTJhCScK17DnOoBm8YLJowzpWPS5NsciYp/SZSvb6IWDTciiK0F1OnHmTm5lOzdQmpg+5Cu6OomcdwAxNKt6HpnkNDyE8s4FWoXMsWSyUFGAc/JO9qujYUSYqMl11FxH0SYysKjs2VEMVQU69yzaY/YDc0lT+frie4u0MqeT611GfaxgAAyz4Ks/rnlmZCWfXk3HgNme27cMcv7mXq1If4y5svU9GoGv1NLAhUVFRUVFRUvj2JBEZTR82Eva/w1raDinMTtTHFhUWj3lq+fu1XhKuPo0vOwn/kAHpHBzzvlyGG/Ah6k7xucS+9mxWjdS0yvLmlhhaJgsq380lxZCZsAbMZtQSOHlSsZdJvvl+qqFv5JM6Sp7EbNHg8XpJSUtAPfwitPYOqskLq1xcSaKgns11npj30KKPG3EbZmrdZuOApHKe4iGTkSN+H86svSWn2+VtTuq5Y8Z/Tfuc/7T+Ype+8rqimm7DaT87YXmdy61RUzilqIHyJ07y0J26/A/Du1k1knGIDYP3xjXg+WIfWnEQs5EejN1G5fDb69PaYOvZAY7TgGD5dthGwNhq+O4Y9SOXy2S0mvqinCodYS3Ez4YMJpTHcGTcQOPYR1RuKEIwWxEgksQeeyQo0CTFE9r/JirHKoHJZToSct1cSjRogGmpV9t+18XmibieC3ojGZMPecyQNB9ajT+vQqoJjcyXE+HnW7AGEd7/EAz2istpzd4em1Z7gREFy345auUd4wjV6pm0MyLufk6/X8cdDKdB3MosXL2Tq1Id4e9W2S0LNU0VF5dImhJbmUjZfuwIXbCwqKhcS51dfkuZxcWLpfXJVXFKvcdQ0anTs3LaelxcvwOPxIIoix1/8BbFwCEEAMRRAMJio27+CaN3Xyvat1QVEar5CDAfQ2tKJNlTL6xZ3dVVCkc96t5sUaJbBPY5gMCF2HciE0lKW5UQUrhMNUSMNq55CDAcRDCZsV98kbehHIxANkmkSKM41N2adQ0woXUyk3/1kjJqJc/Vcfj3rGcWG+6GPPyQaSCySFfcJbr6Ga03pul2nKzgd/9i9lWVjlaXey8aauX/P37/VPVRROReogfAlTGv2O3oNLcpW0oc9KIlUBX1Egj4EgwUMkrmbGAnT8MkO7D1HYurUQyp7aVR8jitBJ5r4wvv+zIpbzS0C19Er3iMz92lFmU/cO6/536zZAwFpAtXa0nC7nAqbIogrQUPW+MdxlhS0IuLQgbQhU2Tlw/gDoX7/CmzXj6CqbD4xX91plRDNV/WWe6Pr6z08PsDGvD0hcpf7qA/C7av9/OWUXcxJ1+vh00irQXJ5VQxfWBLcaq6Q6K5z0rGZQnQiLz4VFRWVc4kvaMdi9OC1dqYuEKX8c+G0gjQqKt9n7KkO6va82aKdy57qYOe29Sxa+AxRrZ7MW+co9EWaV9FVlc3H2LZbU1bYVYHGmiKfc+KVqehS2sjrluT0jIQZ3qTk5BZVa3X7ivEc3Aw9chi7YSfuaidWgwZvzCCPKT5m76E9GNt1R2tNxWaUtFNOXZeN3fAmlrv+hBjys3jxQg59/CHvv/93Kr86gqA3K8YZJ3i8HK3Jyg039GLn5kVyG5e5a29cZfOx9hgqCZHWVKAxWsi7bfxpv/PWeoQ/+7LyHN5ZFZUzQ3OhB6Dy7Wle2iNodVLfytBpuF0nEwd9NRUIBhNo9WhMFtJvmUbHGauxdO8PQP3+FdLuqKuihdF73N4ocPQgYjRC4OhB6uvqEmZDG4JRxZgyRj0MsSjO1XM59lyu7HOXeuNkAkcPUvnOk4ixKEkpKew9FlVcT1KCzpA+W8+RVJXNV4yhqmw+4eoKqjctkoNgiFs8mWn4cBPWbn3QGCUlxOY0V0Js+Ne7WLr2ljLEepG9x6JkZ2ioD0J2hoaTDSK3rvBhKvAwbWOAygaRP74Xorwqxu2r/ew4EiEcFdlxJMLtq/2UV8WwG5EFt/RaQS6btps0nFh6H5XLZ+Os/Jotm9ac09+FioqKSiJ8QTtHasz/VZVVReX7jqDR4hg+XbFWcQyfjqDR8tbrLxHTmxXH/Yf3y2rMzdc2gWMfUbf7DdIGT0Gf3kFxTsRdSXKfPHntpOt1OxPWRhTrhbzVfup9Qer2vCVXrQlaHan9J2LrMRTPgQ3UVVeBwYIXi9yi1nzMGqOFuj1vSUFxUGylr7hKtr7UdevPu1s3Qd/JdJxRghj2K8Ypr69KC7FcN5yde3YysN9A2PsKFQvGYqx4j+t7XI33o82kDZlCxxklZIzJp3jFan5y7bWsLUns/9q1c7uEa7wko8DKNdvO161WUTkt6mPwEiZR30rE40IwJrZAktWRNRp5gveW78JXvrNFtrZuX7EiC3yqBZPe0QGrIbGNUHK60szc2D4bMRIia/xcaWK96v8RPP4xxxaMRTBYELTaxl6baiaU/lFRBhRXggZI6ZNH/f4VUhl0fRWCzkDa0PsIff053s/2yiIQTUJeo/B+tJXUGycjGK0t1LCrygqJed04Vz+NGI5g/VFf0m++D2/5DvJW/57J1+v5yhNS9AAXfxRmzg6pnDAak4Sy7r5eLwtndXdoWDBU8g/WPlWf8IHkCcTIHDNFHkfRwmeZGgiovcEqKirnjUqvZPz5tSugBsEqP3jcrpP4momCJvcej6VbH5zVldQDIigSCq31xYpBH+m5+Zg69SBcozxHn94end1BSv875fYtrSWJMWtFPPX12IxaIh16ofn630TqWiYw9GntEKNRQEBrTk54TnOXDG1SRqvrsqTkZKpKCxGDPjwfrCMzN1/OZAs6ozzOuIuILjkLURRJ7T+RQKdr2bz2GR789WwG3jSCmgg8cs8YHCOVlYcZOTOp3rSIOY9Lir6nCmdNmTGLCb99gGWyDoxUJXf/T/Q8V/Qqw2+f9G1vp4rKt+a0GeFAIMDevXvZtm0b1dXVLY4Hg0Heeeed8zY4FSU7t61n8p055NzUQ1IxTHW0yHK69xVj7zmq5c5eWSHGy65C0OkRw8GmnpVmvbPNdzk9H6yTyl+aXUdrTSUW8pM+YjptJ72IVzQm2N0MIHYdKI/HW74D9yt3IYgxglsXYr92KN5PthNxOxF0BohFZA9ia/YgIv3uZ+wGG8aCBnLe9hHpdz/W7EFAUwl1u3teImv8XBA06OwO0oZMIeZ1U7NlCccWjKVm6xJS+t9JSp88og01HHsuF88H69A7OlG9aZGUlS6Zi63HzXT8TQmZY+egsVhxlhQQOHoQS7d+uP0ib38cxh2AvFVSxvfNf4V4ZFuAl0eZCc6286teBgDm7QnhD0OmVWDRMBPjsnXsOBKRBbeas/eYlPlWZPGHSCXSKmeGOj+p/FCoiZzd6+NB8OEjtRj8ahB8vlHnpoubndvWo7WmNGYzV5M2eAp1u9+gbl8xme06k9muM7rkLMX6Kp4YaI6UYDDJgpwao4VjC3I5sfQ+arYsIeZvoPLtfOr2vEVK39sxd+1FLBqmvt6DxurAp0vBf/jvoNWitaYqru8t30XdnjfJvHUOHWesJv2WaWgsydTtK1acc+KVqSCKaKzJOIY/hGnI9JbrslV+6j1eYj43+vQOUoucxyVnstOG3odrQxFaayqX3fUCWePnIsYiaHSSooCxfTaRQAOLFy9k57b1AJw89kWrQbmtUUH6VHJyb6XSG2PaxoBcXVdwo4nHBxj57Kjz7G+sisq3oNXH4X/+8x/uueceamtrAYhEItx777088MAD8jkej4c5c+Ywbty48z/SHziJ+oGDG4sIlBWSOqrJ0ihSd5KUBP7AMa+bqLkaMeRDY02RM72n2+X0HdpN1O3EuXouYsiPxmRFFLTo7A4AbNcMofKT7eSWGqh312EzStlOzcHNmDpdJ1sFrJCtAhqYUFqKOxxAm5SBY/h0SayrUahKyjwPguxBGI8exFkyF6s1Xc7yutYvAEHg2IKxaG1poNHKlk36dKlPuHkWPHD0oBQ4T1kqZ4lT+t9J9eYXyWzcwY2LU8R8bgS9mcqVT0I0hAAcut+GXitQ/FGYaRsDHHXHKL3NInsLL/soTMn4JqGw297xcfdaP0fdIklmHZdf87/ctf5fvDaimdDFKj/6gfe2+L4rGgU6VL4Z6vyk8kPjyvZSb53FeObCel2McKTGfK6HpJIAdW66OFn8fAGbN68j4m9AMJjJHDu7hY5K1ZoC7pnxGACLFj6jsIQ0d+3doqrMtaEI0FC19lnCziNkjJmlqKyz9RhKSp882YpI0OowdeyB/8gBxFhEYUdZtfZ3uNYtwDFyhtQjvOctuXIvPsaMnJk4S+Zi7nStLGrqGD6dmi1LFOsfr0ZDbulS6uvqsNutNMTsZP7sEcXY4naY8dcIGq2i4i+l30SqNywEGtvI0jtgHzyFt15/icIBI0jOSNxTrE9vf1oF6au6tGdR3xpFxnrHkQjdOmUmPF9F5XzTakZ47ty59OrVi3/84x/885//ZNasWSxdupQZM2YQi8W+yzGqkLgfOGXYdMxms9y3wd5XSHa0kdWP2056kU4zS0kbMgXBaCHsqkBrTUeMRXGtX0Dg6EH0aYl3ObW2NFL63YEuOZPMsbPp+JsSMsbMgkgYZ0kBRwtHST0vqW2p94UQRYEG0YSx03UggnPVUwS2FLGs0Sog3iO7LEeDzSjIE7w+vT3mq3onzGCLQT/OkgK5rzgWCeMY8Ws6zliNoNWTOeZRUgfcJfsMt+wfLiTaUMPxP9yB7/O/kj7sQao3LYJYlMrlszm+ZBI1W/9E2uApOIY/RJLFgBANkmQUAch8zoPmyXoK9gTJ72fEG0LhLXxq/+/b4yxoNZDsyMQ0+ik+++Io1/QbzeQdKZgKGpi8I4WA0YHWntHi+85s1/k7+R19X1DnJ5UfGoK+Se/ZQPSM/lP57lDnpouPxc8X8O7WTaSPfpSOv5F6YhNaOgZ9sg3l4MHDEP0NOFc9xbHncgke3ETMVy+vSarWzMNyZS8yx+YTOPqvhJV1/sP7m/6dMxMxHMBy1f9DEDRyECofH/1bxFhUvn5rZdBi0I9z9dPUbH5RXkedWpJtzR5E8uTXEAUBvzGdjNGPtBibGA6c8poBtJ28GASBtpNeRGd3IOiNOFfPpXrj8yT3Ho+xfTbOr77kr9vX4/P5cG0oatFTHAv6qNtXTLtOV/D4rEfoeWV7rmybRc8r2/P4rEeYMmMWd29AkbGetCHGb6bffX5/BCoqrdBqRvjgZOy1ygAAIABJREFUwYM8/vjj6Bsfvnl5eVx11VX88pe/5OGHH+a55577zgapkrgf2Ng+G2d1pcLzbue29Sxc+CyOZruWUr/sSDwfrANE7NcNw/vxNpyrnkYMB1rtna3Zshjb9SPkzGn1liWgATHkQ58meRM3HHwXMeRHl9IG8+U34P/Pe/IuZ8WCMfTtqKX4ozAFe4KyhZA3KJLWTISrbvcbWLIHUrNlSaOglxlTxx5kjH5EzgSbr+iF7/O/4my0DIh/fkGrk/uXGz7eLh8XjGbsPUc17caWzSdUfVz29Gu+o+v7fB/WL7axIkdD3452ntwV5I/vhagPQrIJuqYJPLItoOi9Ka+KKWyR8vsZGZet44takfRRE6Vd0qHT+OfeV3jljc2K+7N48UJZfTF4vBzPlkVMnfLQef4Ffb9Q5yeVHyJd0vxnHdjWRFBLo88j6tx08bF58zocox9tZv/TIWE2M6tdF0B6Tu/cs5OMZurMtWWFaMx2xfqheuPziLEYYiiQMGiNu1LE/x0Leqnb82aLIFQ+7nOTPmI61RsWykmKFlovRjOZubMVlpat2Rlp7RmtV/2Fg624cEgCoq4NRaDVEzj2EcbLrsKaPYDA0YNktuvM6rdeInXkTKLeWrn3WZfShtRBd6OzO3CVzadNhyy2vPM6JeNM9O1okiri3nkdgAeffI6pC+Zx+Muv6NalLbMfu5OfjbkJ75nfWhWVs6bVjHBycjJff/214m833HADL774Ilu3bmXWrFnq7uZ5pnlPsNaUWPW4eSYx7ikc9dZRtWYex57LVfTLikEf0YYayRt4xK/p8GAxCGDp2lvahVyQS82WJdh63Iw2ORPb9SPwlu+kZssSarb+CUGrIzN3Nh1nlJA2ZAq+8p3YetyMPr0D6bdMo+GTHYpd0eT0TJ7cFSR/e4BFw0wE8u0sGmYi0yrg3rcMkHYhU/rfie/QbsLVFQh6E/aeo8gcO7tJFXHEDLyfbCcW9IMgACAYTC2+D53dARotgtFCZu5sUvtPVOyAhr7+vMUurGP4dCh/l2U5GgZ10fFOeYRlH4VZ/XMLgXw7q39uYeeXUaIx8IZg7AofuW97aWMTFJ8pf3uAJ3cFSUpOknua47unzRl40wimTn1IkcWf/tAjqlDWGaLOTyoqZ0aWVUfXLqkXehjfe9S56eIj4m9QBIPJvccr9E8CRw/i2byIO34htS299fpL6Lr1p2brEo4tkLK/oYC3xfohfdiDeA/tkq2HmhMvE1b+u4OkTG20tNJvbKZ6QxEaowVdWtuElXL2nqMUlpbxz3Pqua4NRcTCAbS2tFZ7m6tKCwkcPYj3423UvXQHzrdnYfQcw7X+OVIH3EXm6EfQmpMInTxM4OhBassKCQT8VB0/QtWaebjWPYcY9JJ1WwHt7n0Z29U3SuuqUQ/z9ZHPKR5rUlTNFY81sWbZn1myYB5TZsziq6rD7P3Hdn425qZzer9VVM6EVveFR48ezaOPPsr999/PTTfdRFpaGgC9evXihRde4Fe/+hWHDx/+zgb6Q+PUnuC6fcW4yuYrMr2ezYukoOqU8zs22600deyBe/9yXOueQzCaESNR2RsYpJ3RwJcH5J7ZOOZO18o+wlVr5qEx2Um/ZVpCb+JwzXFMnXoghpSG7LreE/nDlt9T8nOlp13xrWZGr1hFoNN1GNtnSyJcYT9JvX9O/d9WktInT/FdxHcvNZZkMnJmEvG4qNn+iqJ/J57JtnYfQMOHGxPvgIYSl0M5AxH6dpT652ZuCWDUweA3fXR3aBjUWUuSSeC10WZFn+/knnrFZ1qaYyZ3hR/9kGnytVsreY6XXsWx2+14PGfe9/dDRp2fVH4onK1Qlsp3izo3XXzozDZF9tOaPYCQ6xjO1XMhHCCzXWemTn1Ifi5XHj+CUF2N1mgBBDQmO2JMJOJxKa4b11NJHnwv1Ruflz2Am/cIyxonG4pAFKlcPlvSN2nWDxw/336DsopNa0+nas08YgEvgsGMGA4T83tkm8uqskIyRs3E0q2P9HlKCqSKvfQOpA64C601FWdJAc61vyNz9G+b/Ik/KEMMB9CY7DhXP02mPsCKW82NWi5RJpT6iBAj4nER9dYhhgO41hQgCFrErv3RhnfiaPysxxbkJl5XBWMJXTN8YVjct4a7H/8NRt1cxo9TkwAqF5ZWA+EHHngAvV7Pyy+/TMeOHenVq5d8bMCAAbzxxhvMmjXrOxnkD5HmPcEAqf0lC6Hqtc8QDXhbTNynnm/q1ANL9sCWBvClhUQ8LlkkKlxdgaAznvI3SWArXC35CccCXmJBX+LSn5oK9OkdABSiVyD1qRxbt6BVr+HAKWJeqf0n4j+8vxXrJzOCRkvl8tlojBbs149QCILpkrNAhMCXB9A0Zs9l4YjyXdTteQtBb0x4bZtJCnBPNoiEY/DGqKag9/bVfu6+Xt8ikJ+2McDcG5WfqT4gktlM3Kv5RoXKuUWdn1R+SMSFslQuftS56eJj6NCRvHtKIsF7cDPDho1m6oP5Lc7X6E0IegPptzS1MLk2FFG741UEjVZeJ+mSsxAMpoTWQzF/A54Pyqj/2wq0SZmI4RAZjcFo8Hg5ztUFknVjKIBgtGDvOVJe55k69cDWYygNB99VCHC51i/Ae2iXbHdZt69YDn4FnYm0oVOxXd20MBGjEUkg1ZxM5conIBpGY00mM3e2fE3fmjkU36pMVizLiZC79mUaRCOZzcrDXRuK8H68DceIXycsM/eW7yCy/03c1U7sxsQ2Tt0dUvXdq8Mj3Fe4gNxxOYSsnTF4K6gLRPnaFTiPvwSVc40QC6MLnrzQwzgrWg2EH330UebMmcN9992X8Pi1115Lfn4+kyapvl/ng0Q9wSl98vD8bSWlzXqCT3e+//MmA3hoUh2sKi1EozdiyR4IIoRrKqjeuAhBb1D0/0YPeqjbV4xgMKO1JLfqTZzcezwAurS2OEskhen4NWwGTWJPu6QkfI3/FqNhtLZ0oKnEp/nOqtSrolPsnro2Po+h/52SjVP5Ltx/XU7Md1La5bSkyH3PzZUVIx5Xiyyya/0CRMzkrfJiM9DigfCXsYmD3kMuZWnb3mNRbEatLNylM9lkzz2Vc486P6n8kGgulKVycaPOTRcf8WB389pniPgb0Jlt3Dx0ZMIgGEBEJOMUxWbH8Ok4Vz1F7a7XFGsIZ0kBVaWFZOTM5LK7XpCr8ZJ6jaX+vTVo7Q4EQYNj9G8V18scm4+z5Bkp0xv0taiEk9ZvM5VjGDED17oFigRJvHoPkB094sTLsdMGT8G56inQmVtc0xmMJkxW1LvrybxtXoLv4OmEZea27AGYD5UqtFYmrPazbKxZ4RlccKNJfo/P3zqBL2jHYvTg1Lal/JhO9Ti/1NCbiF52xYUexVnR6s/t3//+NyNHjmTevHn06dNHcayhoYFnn32WVatWMWDAgPM+yB8ime06Jww8W1MXTnR+uKaiFZGEAJZrb8ZXvpP0YQ8S8bio3flnRea4euPz2HoMxfPBOsSQn6hWn7AU2dTxGizd+lC7+02CJz5T7DRWlRUSC8bIWx2guJmBet4qP56YjYxhU5Q7neW7ZOGr5jL+sVAArckmWS01Gt87hj0oT/61W1/CZtRQL0CS1US9340YDEhlT2KMzFsfS2gRIBhMIGhI6jmC+g/LcNb6Ez4QEgW9doOketj8M3k1dtJ6j8fSrQ8VC8aqQfB5RJ2fVH4I1ETAZpfaNrqk+S/waFS+CercdHEy9cH8hIFvXFvF+dWXZLbrzB2/uBcxHGxVYMrRbD1h6tSDzNx8Klc+LreJ6dPbk9L/Tizd+lC/fwWIYkIF6IjHBWIMrSWZqCh+4/WbpPPStFaKi3I5Rs5osUaLW0Ya22cjRoIg0uKayemZ7D3W0CJZYTVqEpeChwMJy8xjH6xk2XiTfJ0fZ2gRxRA5b/vwhqCtXWD+EBN51+jl9+jauR0gWbt97QqoQbDKBaFVsayVK1eSm5vLvffeyxNPPIHfLz2Et2/fzvDhw9m+fTuFhYUsWbLkOxvsD4k7fnEvns2LWhVz+CbnC3pzQpEEMRzE//l+Wdiq/u/vyJnj5iIQ/s/3IwZ9kopz++5EPTV4V+dT8dwYfGsew5jRmUjtCY4tGEvDgfUtrpExaib69I4ErhlHzvIAxgIPYzfYqIpYyGhUcGwuiFW9+UWOFuZQt+ctokEv9p4jiAV9CBot6bdMUxjfRzwuwtUV1O5YSoYhQElOiGC+jZKcEFmmKFpbKpljZ7d4oDVZBICgN2HqeA2mj97h5o4huZSnOXuPRUkyKKX+80qC1Ed0jFkRxDjXQ26pgejAh8gY/Qh1u9+gbl+xaod0nlHnJ5XvO/He4MscJjkIVq2QLn7UuenSQXZx6DuZDjNWQ9/JvLCoMKEYZ/B4OYLemDA4JRrBfFVv9OntCVcfx71/OVVrn0VjTcEx4tdy+XBzane8isZkIf2WaaQNndrCikgwJBbU0qW0wb1/ueJv+rT2ktZKwIdz9dOS8GmjUKo1ewDB4+VojNaEol5i14HkrfIr1jgTSmNoe47Dva+4xftrbWkthMZih3fjDTX1BBd/FCZ/e4DiWy3UzLSz7U4LURE+qYrK73H3RpgyYxaV3ogiCG4uEjv5zhx2blv/7W6uiso3pNW9F61Wy0MPPcSgQYPIz88nJyeH7t27s2XLFnJycnj00UdJSUn5Lsf6g6J5729F405l857gROcf+vhDRemPGPLj2vi8LGogZV6LEAwmxW5ja/L64eoKtLZ0HCN/jbNkLlmWGMW3NpW5TCj9lEi/+7FmD+Jo4ahWr+H+m9QzI0ZqqHNVycdOPVcM+eg4o0TuZW4o34XWZFP0o8SD9OpNixD0JsyhGpbdZlH2t4yGnLdriHprEYwWji3IRZ/egeTe4xUPhYxRDxPY+CyTr9ex9ECYaT81MKnUz9IcZSnPwM5axiz34QmCzShA95sRP3yXlE6XY/7pJEUvMoKG+v0rEBxt5An81N1mNVN89qjzk8r3mXgQ3LVLqhoEX2Koc9OlQyJtlbThM6gqLcS1vgjHiGYVcKWFiLFoQv0RAM8HZdh7juKyu/Lkijhj2x/J7VKn2lSK4YCsQg3KajV9egdMHa+RS66bZ3iT+95O9YYiWYukqmw+sUADlW/no7WmorGnEfVUY+7aG/dfG4VSDRbEoI/Um37ZovXM86/NuH0iYzfYcFdXkZyega7fRJK79cO9fyWBowflc+s2FmExaPHUOHGtKSAa8CHoTaSlJpGelcreY0EGddFRsCfI0pxT+o7HmhmzIsi8PR5+dEU7Zs6dwXU3jgZQBMHNRWKDx8uljQpQ100q543/WoRw7bXXcs8995Cfn09FRQW9evVizpw52GyqeMf55lR14dMR971LH/1okzLg+2VYswfKE7HGaCUW8ILWgGAwyxN6ax50upQ2iLEIUW8tSWYDxTm0EFQYu+FNAtZ0efcy0TXaTl7c9CCJ6NBakhL716V3UJjPV29aRMRdmTBojtRJzfleSFjO7A2Bb/srsqhEvK845DqGr3wnsUAD/o3P4nHXs+ZTDcvGSpP2jzO0sj9wshHu+4mBpQfC6ATYdqeFu9b6qf9sC1q9kYovj9BhrDQ2b/ku6na/oXjA/OEPzyFGwqSOmqlO6ucJdX5S+b7S3OpIDYIvPdS56eInkbaKsX02MX89jpEz5LWToDdjv2EU3o+2yeXHzfVHmgeqBkdHrNkDsPW4WSFWKglbzUUM+qW1VYJqNUu3Phx7LpdwTQVRbw2iKMjroHjJtdaaiqA3c2xBLlp7BmIsStbPnlSoT2vt6S2FUsvmE675qoWol6DVoXd0xDh4Ch2brcmkqkIj1WufIRJoIKtdFx544Df0GDCCLz7eyYJ5BVjHNK2vKtf/jl+URXh9FBxyKRWjiz8KU7AniCcQI9miY/rM6Ywal8ORGhDDYfm8RBsTDJ3GW6+/pK6ZVM4brZZGA5w8eZJf/vKXzJo1iwkTJvDnP/+ZkydPMnLkSHbt2vVdjVHlG9B8AvF9to+GA+sRQ34aDm7G3LU32qRMMsbMouNvSsj62eOIkQhVZZKHXFKvcS3Kcqo3Pk9KvztwDJ+Oe/9y6t3uhAGn2+WketMibD8elNDHLqXfHYrgVmtJIqXfHS3P3fi8LLoFjcGuuxJtUkarJUoakxW73ZqwnNlm0rUo1XYMe5CGA+sxX/E/ZNk0rBkdITtDo5i0867R8/F9NgL5duoC8PzfQ0y6Xk8oBtM2BjheL4IYxaKL0KFzF3ls7v3LFR7Kpk49SBk2nZjerPibvXFSVzl71PlJ5ftOllVtmLsUUeemS4O4tkpz4v6/1uwBtJ30Ih1nlCCG/aT2n0jUW0NKv4nUbF1CzebFOBoFtZq3lMXLlpuLlQpaHan9J5KZOxvBIPX8x10sTn1vwWAGjY5YwEva4HtAjJE1fi6X3fUCWmtqY0IhjKA3EfPXkzn6kVNa0h4mWncyQavaw3g+WIfWmspld71A1vi5iLEIqQP/D1PHHlSVzT/Fs3g+1h/fSProR7GktlFUs/3phYVYB09TXF9o0506b5ib3vBhM8CTu4JAU5n0omEmgrPtrB5nYN7sxyh5p7TF/XB+9WViK6avvjwn91tFJRGtPmWLi4tZsGABbdq0YdmyZVx77bUArF27lvnz5zNlyhRycnKYNWsWycnJ39mAv8/s3Laev7z5MpUVX5xxGa3zqy9J87g4vmQSUbcTXUobUm/6Jb7P/4rn/TIyx84m6q3l69d+Je1wGixS2c6WJYRrKkBrwLnqKcRISN55tGYPQIxGCFcfJzk5mb3HQi0FFQyQNnkxvs/2oPnPbpxvz8JqAG9UT9ot02RBB2gKbuN/c655BmIR2T4g+NUh+VjweLlUTh0NK3Zg3fuKZaGrWMBLg2Anr8RHcS7NSrZjeAJRUhPt9Aa9aI+9Jxu95/czMnW9P6Gy9eWpAsEIrPk0gjcEgYjIa6NNtE/SkLfKzxUds3hv6yIYPK3V8vKIu7LF3yrUSf2sUecnFRWVixF1brp0uOMX90pVWkOVNkmpA+6Sz5F6cBstItPbo7M7aDtJ0jNJ3A52HDiNWGnIT0yjQQwHcZbMxd6zyTvYtaEI+w2jpESGRpvYlinoR6/XEQ4GQBASvkcs4E383kEfrnULiDbUoLWmIsaiaK2pBI4exNZjqPw++vT22HoMxX94P+k33wdDp/HKSwvlNi90RtKaCWnVbFlM0tf/oPi2Zq1zq/184oyy62iUVT9Xtq+9OkyyTnr1xvGKMZ6pSKyKyrmg1UC4oKCAe+65h/vuuw99M+sGk8nEnDlzGDJkCPn5+YwYMYK9e/d+J4P9PnO2vRH2VAd1u1/HMWJGiwnd/++/S0Hk3r8oSnfjioLW7AEEjh7EufppssbPTejhW+8NkrcqoOgRzlvlpyEE/hd/QYbOx4oxetmQPW+Vn0DNccUY48Gtt3wX1Vv/hKDRkjHmUUXpjhiLYf1RX1wbioiFg4gBD+nDp+Na/3vEaETRLxPvwXGd/Jyct2vwhiDZkYmu30T0+1cmtnvSm3G7nPTtaAekDPCDm/zcucbPG2OaPttda/3U+UVSTAKLhplayP8X32om5+2/IphTiO5chKBL7FGsS85q8R2ok/rZo85PKioqFyPq3HTpcKq2itZkQRC0UpAo9+AWYutxM6C0d9SnJW4p06e1V4hdNT9et68YjTWlRcly/f4V6B0dSB1wl6Q2/bcV0rqnMQlw2V0vSO1uH5RBNERUZ0FjSUbTrMWt+RiEBH+PW2FGG2oQjBZi0TDpQ6ZIyZDqCi7r84LsYwySB3H931YCUhBd6TqJN6UNacMfQmd34NpQhKDRSsmL8ncp/rmpRU9w7nIfnlDi9rXP3zrR4n4k2pjwbF7E1KkPnc1tVlE5LdonnnjiiUQHbrrpJkaMGIFWq010mPbt23Prrbdy4sQJBg0adD7HqCAQjHDs69ozfp3RaCQUCp2HEZ0b5j7xG4yDpkolJhoNupQstI7OHCx7mZzcvP/6+hXFr5I6fIbi9YbMy6nZ9id01jQCR/9F+rBfKY9ndKF6QxF1u98g9PXnRBtqCBw7iCHzcrT2dIIVH0v+cD2GEqn9iqCtDSv+6eKxnUFWfxYjet04sm4rQPzXWlaOliY/rUagS6qGGy7TsmzrQTSX/bjpWhuKiPrqCX5VjkZvlEWw4uMxZl1J3e7XCR4/hKV7f0JffYag1WK/bhiBY//CES89bna+++8rych5BPuQaXjLd2Ef+iCWrv+LxmSjdtufMGR0kd+/qrQQ+/XDMVR9SiAS41cbA0zbGEAQ4GfZemZvDzJnR4hVh8JoBEgyCvx5jFnxuXpkaXl0W5B5Nxl5bGeYlDGP4f/3P5g4fjgHNi5Hk95Zfr+6jUUIkSD6rCvlv3k2L2LS5AfofPlV8r272H+bcc7nOK/qknlG51+M89O3nZtA/Q2cSy6FMcLpx+mPQXqqGZtBg14XQot4Vu8VRUM4asQbjlFTF8B82qaobz7Oi4nzNc7vw9wEZzc/fZd8l7+3ndvW886qt0kZMYP0W6ZhbNud4OG/IlR8QNW2V7HWfUH/vgM5/N42dBmXY778BqKBBmp3LCXqqcZ/5AOMzZ7vVWXzibqd+A7/DZ09A8+/NlG3+w08/yylbtfrhJxfkDnm0RbrmNDJw7Sd9CKGjM4EKz4mdPIw6bc8gNaaSu32V6jdvpRw7XEyRz8qjbNdd3yf7SPq9xD48oByzbahCH1GFzwH1mNsI42tbu9faDi4mcwx0utN7brjO/w3NAYzST/NxXtoD95PtlO7/RV8n+5BY7IRCzQQOvk59p4jCFZ8TODLA9g0Yeo+2oG24j0MPx6C5/112K8fjmv3Ml4eZUKrEeTvtn2SwGM7Q3R3aLg6U0uX1KZJZ/fRKH/zZDL6jnsgFqOmPoRZA50vv4qM9HQOlr3M1+++hLXuCyZNfqBFMuhSmZNO5XyM+0znp3NNLBIi6Kn87yeeIZa0juf8mq3Raka4W7du//XFNpuNp5566pwO6IdKa6IN37SM1ueuwZHIc87txNr759TvX5G4hCbobVJqLpuP5cpeVK2ZRyzoRZ/egdTGjLG507U4SwqImbMgVodlzBx5t1HqH1YKgEiCVTG8JXMRQ370aR2w/vhGPO+X4Rg+ncrls1spGwoQrq5APBQmbci91O54VQqgPa5WSn38VL7zJIIgNJYaFaA1JxFxV6IxJ+FcPRcx7EeX3Ebu06lyfcnSA/9QGL3/fKWPqAiCAA6LQCQKR91iq77Ce49FSU7PkL6DwdPYsmMpvyt4mLnPvEjFUam0/YEHfgN8c+VvlW+OOj+pqKhcjKhz06VDInGm1FEzYe8rvLXtoHxe96uv45WXFlLpOomgNwIiaPXEvHXyGkOf3oG0GydjzR5A7e43pcDzFLHOaL2zVXeN5irQaTdOBiQBLTEWpWbrEjJGzVSMMyNnplTm7K2X2trCwcaxQdDzL9AYpLGF/AgGM5ljZytfP+phnCUFNJTvlm2cmlcTxsJB0m6cLGm4rH2WDL2fZTnxqr8IE0pLcdcFqPj9WGxGTcL2su4ODfn9jC3cOO7eCDPnzkh4T85EJFZF5VygKnFcJJxtb4SgN7VSCmyk4YN1sjDDaZWaRz1MzdYlxIKSjZGgbfp5xPtL0nPzWwSxyekZCQ3Zk9MzMQ5+CGdJAeHqCiL/dCJGJKXE1pSqBYOJzLFziHprce9fTsznRjDbZU/kU8/X2tKIhQIIOp30INDqlRP6+iKsN4zCV76T1IH/B4Ch9oisEg1wskHEYhBYMVppm9QpRUg4uXdJEZhQGkPXb6L83VSsOMLYcTlcf+N4Dh+pVZjCq5O6ioqKiorKuWXntvVnZU/4TRMQ8Wu+8tJC3K6T6FLakNwnj5qtL8m2j77P9uHevxzXugVojBYyxsxSBJ6OYQ9StWZewnWMxmjl2IKxCDoDCFpqd/4ZkAJh975ixJA/YQAd9dZivvKnBE98hv1/RuMt36mwy4wLa7X2ejHoQzCYyRijDJIdw6fjLCmgen0RGpMVS6yBZT+ztHQNeUfHfyo+YebMJ8lb/jbFY02K1rnJPfXkXSO1B8huHGYtvyv6nawaraJyoTmDAqlzTywW47HHHmP8+PFMnDiRo0ePXsjhXFDu+MW9eDYvUqj2eTYv4o5f3PuNXm+321soP7s2FCGKIhm5+aQNva/l8QRKzXGhhERqhhpLMjVbl4AocuKVqZJvLqDrPZEJayNKQ/Y1YXS9JzabbE1SoNrYNxPvtTlVpVAMhyRbgt1vkDZ4CukjpqPRG7HfMKqFiXtVaSFRfz0ag5GMnN+iT+8g+/LJStEjpuP5YJ3cCw3grnYqMr0Fe4K8NloKjPVagUFddCzNMeMPi0xYrTSaz1vl54TfIPsnx7+bDp27nNX9V7n4UOcnFRUVlYuTuK4KfSfTYcZq6DuZxYsXsnPb+m98jdZUo9EZmXxnjnytxc8XULTgKdzVksevpXt/arcvhVgURDj+4l3U7nqNtMFT6DhjNbFgYrGqWMDbYh1TvfF50oZMJWv8XHTJmWSOzUfQ6qnd/Qa1u9+UfIXTOrSqbh2pOYGtx1A8H6wjWu+kZusSare/IiU1/G4EvQHBYGpVoVoMtxIkh3xSP/OYWXjDQsLquHq/ZHheWPg4I8ffRu7KIMa5HkavCFLvuIalB2HHkQjjsnUsGmaio8PC74p+x63jRn3je6Sicr65oBnhrVu3EgqFWL58OR9++CHPPvssixcvvpBDumDEdxz/8ubLVDSqRp9JGe09U2ewaOEzsuecLjmLqK8erclG5fLZ6NPbY7myV6NiYDU2o0AsJBLZ/zpeYlizB8niTnE7pVOVmjXWFNIGT1GUz4ixKDq7g8qAjjElAp6GBpKSk6kPglj2e7Q7XkMwWuQSobp9xbKxfHLf22UDecGcu9DSAAAgAElEQVRoRoyBxmzHva8YS6P/ccRdReatUhm2wdFRUjV0HcNmFBCDIjYD+GNRot5awjWJlZvFoE8hfmE1CDy5K8iaTyMccsUQxcRiDk4v3P8THdM2BiivimE3aQgJJkIaI0nW9KZSptJCUm1GVr9TyvWnqCCqXLqo85OKiorKxcm58JyNizP5u/XH//l+wjWSo4b1x4PgR31ZvHghhz7+kHe3biKjmWeua90CxFiEzFsfw9g+mxOvTJV6bxvHok/vkLgCz9EBc9fecsmyLqUNyX1vR2tNpXrj85iv+Ak1W5YQqZOyzp5/rkWblIH5qt6ySFdzkS1rtz40fLSV+v0rpGsNvhed3UFV2XxsPYZy2V0vEDxejnPNM1SVFZIxaqbi9WLI3+pYBb1JtmBqreqv2+Xt5H8XFj5OYeHjvPNOGQW/W0zFlx9jdTj4v81hKpx1dOvSlllzp6tBsMpFxwUNhN9//3369esHwHXXXcfHH398IYdzwRl40whGjbkNj8fzjV/TvDTInupA0IAbkai3Bo3BjGNkk4q0s6QAxBhtkg0sG61rLGFpYELpH6lzVeA5uJlYwEv1+iLQGXCWPIOg1ZKRM5OaLUtIGzKlZfnMqqdAo8XecxSp/SeS2jgu09GD8mtcG4qIemvlHl0A5+qnJdukuKeeoMF2zUCM7brjKntOLvFpXoYtZXRj6Ha9wLIx+iaZ/rV+qra+hNaWlrh8OjlTtgXQmu0YoyJLD4TlHuHuf2xIWAKdnaFh0XAzO45EuKfMT1RnYtbcpxCB/McXcOyk9LBKHXQ3OruD3+bPZ8YsA5dfPfBb/wZULh7U+UlFRUXl4uRsdVWgSTX63a2bcDRTcq7e+DymDj/GPnQam9c+g2P0o8q1z8gZVG9aJP8t4q5Utov1Ho9r4/OKMmXXxuexZg+k4eBmxEbtu6jPTfWGIvTpHTBf8RP8/3lPGeyWFmLIupyGg+9i63GzbHcpGCwY2nTF9++/y8F4cyeQeJtbav+JmDr1IHPMo1SufFwOwDUmK9bsgQSOHcTcNXGQLYYC8mfS9Z7IhNI/siwnoujzHZwzgL4/vZHPvviKJLMOtz9Khy6XM2RQL/66M8BnR06Q0aUtSxbnqwGwykXLBQ2EGxoasNmaRJa0Wi2RSASdrvVhaTQa7Hb7Gb+XVqv9Vq/7rmltnFs2rWHpy4uorPiCrA6X0+un/4+dOzbj8QfIyJkpWy55tiwixXEZDWFRsUNp6tQDrTkJK36W5YRa9HrkLH+HWFCanbXJmdKEfWC9XGrcarY1LJmmp/TJa3EsXHNcDpilh8AA+dz6/Sta2AhUb3weY7vuCEaLrBB9ai9xZP+brBijV45/NOSWamgQtQkndFuPobJPn7dkNll2gZdHNfUIPz3IxF1r/bw2WmmfNHeQUS6HLrrZRBsbTJu/kAPl7zFv/ktobyvA1KkH3vJdjYH2SQryH2Xqw09x66ixZ33PLzYulXGeK850fvq2c1P82pfCd3spjPNSGCOcfpz+QASz2YzdbsKgjWDRnV0Xky8SQ2uwUx8LYDYHsJu++aP/+/B9qnz/OFees++//3ccjZlPkNZK6cMexLXxedrd8xIRf0PCtU/E3aSUe+o6xZo9gJDrmEKsSgz58XywDjEWI+vWOYo1irlrb/yf7ye9cd0TH0dGzkycJXMRw2F8h3YTcVeitaYR9dcTOvGZXC2XaNxxP+P4eIlGEKMROv6mSf/FW76L2t1vYM0e2CzINmPq2INI7Qn5M1mzB+EFckuXUl9XR7cr2jE4ZwBbS9fw6jDomyfZZk4ojeHSOlgX7xfOs7H3WC13z34MQA2GVS5KLmggbLPZ8Hq98r9jsdhpg+D4OWeSMY1jt9u/1eu+axKNM5HHcFlpIbGQn6xxjytLg4ZMo/Lt/IRG6xF3JfWIrSo8CwYziGDI7CJN2EGffI3WxK10KW2Ihf3U7StWeNDF+1egSb3aW74La/YAqd/YZJXLbuJjTx/2IDVblyCGmt63uW+fsX12Y3+vcrHTt6OWercbEYGk//0ZzpICxKAPjSUJy1V98B3aTf3+FWiTMoiFYhw5xdcu7xo9kZhIzts+vCGwGcCkhbvWBkgySo30edfoCUdFDr15nCt/1JdjX3yBvnYJpk49WuzivvKHQgzR0Dcuz7qUf5vfZ850fvq2cxNcOt/tpTDOS2GMcPpx+iPg95uImmvwA1GiZ/VeIbT4gjr8/gh+vx9P+NyM82LiUhmnyrnhXHnOtpZZjrqd1O0rRme2JV77JGfJ/07uPV5uJ4uPxVe+E9vVN9LwyQ4yc/MxderBiaX3kTZYWVmXMephuULO2D4bb/ku3PuXS3otae0Rg37MXXsRqW303RUEBJ1BsT5LNO74+is+Xo0lCQRBsVaLB+ye98sQw36y2nXhhht6sXPPTszd+iuy2lprOgGdmT+89Bi3jRtB35/eyKvDUCQlJl0TYNE//kFVSGTaxgD5/YzkXaPn1WER7i8sUgNhlXNCLBbjiSee4LPPPsNgMDB37lw6deokHz948CDPPvuspJGUkcH8+fMxGo2tXu+CBsI9e/Zkx44dDB8+nA8//JCrrrrqv7/oe0jz8uasDpdz+8R7FEFUol6YjJyZOFc9RcTj4sTS+2SRq6Re4xD0JrTWFILHy2X15XD1cQSDGbuBhGXAVr2AV9AghnwEjh3E3nMU/s/3yw+AUwPS5mU4WmsqzpK5mDtdqyjpEbQ6vOW70FpT0aW0wf3X5XIvTGtiEuHqCoUZfDyLHO8ltpu0CceflJxMvTeA559rEfQmRK2BmK8e74cbsRtFPIA16MSv09PRFmlxjfZJGjola1g0zMSY5T5cfkg2QSwGL440U/xRmDk7AogiuGuqcYx4CK09A2dJgfyQi9+bM+1TUrk4Uecnle+aGkl7hq5dUkm1NABaDGcZBKuofB+JP1/P1p6wtcyyLqUNDQfWccvNOezcvEgRcLvWL0CMSGJXUpCYStTvlTfhBaMF248HkTZkiuTn2xjgRtxVsmZLcu/xWLMHyLaRgtFM3b5ifOU7W1S1BY59gs6eCojEQn7sPUcq1menjtvzQRn2nqNkHRPXhiIErR7HiF+3WKt5D25m2LDRTH0wX75O96uv4+XFC4jWV+NdnY8zJGI3a/nZhNu4bZz0/X525AR985qSKsUfhVn2UZg145XuGwDjsnV89taJM77HKiqJOJ1+iyiKzJkzhxdeeIFOnTqxcuVKvvrqKy6//PJWr3dBA+EhQ4awb98+brvtNkRRZN68eRdyOBeERNnexYsXAk0TfWs7lmI4SN2eNxW7kK4NRZgMeqJhP861z6LRGxXHnSVzySsJUZyLQubeK5gVnndx4YZ48Gvp1kcq9Vn1FGIkhD69vazELEYjiCE/zlVPI4YDir5Z14YixHCI1BsnUb2+iJotS0jpfyfu/ctbEWiQynJc64twjJguP2QQY9h7jsB3aDd5JT7F+CesjeAJ6bD/z2h85TslEa71RRgc7UkLHGfZWEvTuav9HKsTuXONnzfGKCfsghsl6f+GEARnS6U+d67xs+dYhPWHI81Kp0NMKF1MpN/9iux183tzJn1KKhcn6vyk8l1yahCcYlKDYBWV0/FtPGdPtVy64YZebCotJCNnpmL9k9z3dqo3FDH1wXy6X30dv5//JGLYj2CwNFacJUuBb8iH1paO1mxtsRYzXHYVuuQ2coCb2VgSXbevmOrNL+Ja9xy65DagM2DvOQrPB+tabKxLfr9zFUKl8fVZVWvjXl+E//B+6v+2Ulqr9ZtI9YaFjUG3Xyq3DvpJdrThkUee4H/73KT4jj795ABhTxVZFii+tWmd9IvV77Dqp9dz67hRdMhMYe+xoJxQKNgTZGmOWZEhXppjZtrGAG1sAt26tD0Hd1zlYkMQoug1320lzun0W44cOUJKSgqvv/46n3/+OQMGDDhtEAwXOBDWaDQ/eFP5/6Z8uHPberQma+Kg0WjBMXx6CwGrwNZFTL73IX4//wkcox9RHM/MnY3znSflMmCrAbxRPZk/m52wTDml/51S70h1BbqUNoBA1vi5LdUQ0zoQqXeS1dg3GyfeH6yzOxD0RsSoVJNn6thDVo9unkVG0GL9UT9CJ//dqHBdgz69gxx0G9t1x7VjKTlv18jj9xvTSBs8CWv2AMydrsW1bgG6lDaYPBVM+qlB9q/r7tAw6Xo9i/4RwhMUGbHMh1YD3hB0ShGAJhP4uI3SG2PMjFnuY834BB56G96UbQ3Otk9J5eJDnZ9UvitqImCzm7nMYaJLmh81E6yi8u1J5C8M8PLiBTQEQgpdlU2lhYhBn+y4Ed/k11pTyWon2SIOvGkEWzet4aNPP22xZkkbPp36v7/TouQ5LiZqtyfh+aCMzNzZsqaIr3ynIvFQVVqIPq1dqxvrYsifcH0maHUJx61LaUPbSS/K1wgcPSi3t2mMVmzXj8Bc8R6vvFHaoqVgyQtz+eeW5bS1odBSGdRFx+uj4P7CIkSg0htjwtoIy0ZLSYlDrlhC941Drhh3b4RZc6ef25usclEgagxErO3/+4lnSE1NDZMnT5b/PX78eMaPl1xZTqffUltby4EDB5gzZw6dOnViypQpXH311fTu3bvV97qggbDK6ZUP49liy3XDWygQVm98vtUeEWd1JQNvGsGCeY8knlQjIXzWVDLHShN6w4JcxXne8l24/7qccHUF7v3LMV/VG/FQmFjYjxgOtJDhr974PJbsgdTvX5G4Z6XeiWtDEWlD70Nnd0iZap0RW4+huDYsJFpfpcgiV5UWEvO56fibEr56+V6FWrU1ewBaayo1W5Yg1lSQ9qsm4Qf5/RpqMHW+Dk/d1yz7KMzSHGXm1xOEWf0MvPZhmDdzzYpscWWDSKcUgWkb/Oz4MsohVwyLHo7XxxSfq29HLe7qKuz/+3Nc6+bjGNn0cKzfvIj7zrBPSUVF5YdNUxCMGgSrqHxLElXZ/eEPzyFGwsT0ZlkAFJrazFzrFiCGA2SNn9tqv/HJysoWuiYZOTPlQLQ1MdFwMIAYbPLqde9fnlAUq3rTotZtl9I6tLh2uPo4jpEzqN31mmLcVWWFxIJ+jhaOQp/WAfNVvfGW78SQ2UXOYHs+WEd90Jfwu9u9aSVrf25m8Ju+hIHtZ2+doOB3i0ke8Vsi3mrGbnhTWgsZNYnb1kwaZs19Su0PVjkj0tLSWL16dcJjp9NvSUlJoVOnTlx55ZUA9OvXj48//lgNhC9mTqd82DxbLHvoVlcg6CUFQsFoSShSldmus5RJNloSZ5INZsWE3nzy9Zbvom73G4oeFWdJAcRiiJEAGpOVmNctT6iCwYIYCVG/f4Vs2t7y/SykDrgLa/YAvOW7IBYl2uDCf3g/iDGSev8c/+H9VG9YiD69PbZrb8bzfhnB4+Wk9r8zgRJ0ITGvG7QGTrwyVd4NTe49Xt4NDZ08jN1IwlKdMct9rPk0wpu5ymPLxkplPIuGmZiw2s+k6/UcGGCUVaR1GoG8a/RAU19y9PBu7ho/nC07llKx4ghtOl7Onb888z4lFRUVFVCDYBWVsyFRlV3KsOmnDVij3lqS/vdnVK99hmjg/7N35vFRlff+f88+k5lJJskkUEiCaK0alar99XqRfRFlCxBRRAW9inXppUgRq8Z6WyWoYAw0VdBCXSuLEpZAoiwaFErVi7bRhqq9IgkgSSbJZJl9Ob8/JnMyJzMTUFFEn/fr5avMzJkzz5xJn/N8n+/3+/m44vqNq3dto+HwQfISqUc7jyW3brRk4A140aRmya8HmusTq1A7j5E6+Jq4pEdTxRIsg65QHB8JjnPQmNORAn65ek6TmhVRpZ7+P3HrJV/QH5eFrt61jclTr1Vcu05fKGIraU8c2J4zsB//OniQ3ML8SBIifxRpgOujXczcsExRSn3d5iBaq10EwYKTSm/6Lbm5ubhcLg4dOsSAAQP43//9X6ZPn97r+UQgfIrpTfnwicX3ydniqGhU61sv9JgklwLI9kDOqlLGjBzLihXLkNS6OCVDR2Upkt+jmIhjPe/a/qrcrQy5WlF3lWDHBaJaPWq9EXvX5OrcuyZhuXPG2NvkINj51guKybixvBjXR2/I/cBRvz3J75Ezz2lDr5fFsjSpWVgGXUHH/q2otDoyr5yr/G4x/cgdkHBHs8NHr2U8sUHxotFGRg3U8twUE7dWeJier5X7qnVmC0seXsD0rknej4aDLSY+Pdj6jfytCAQCgUAgSE6yKrvohnnCjGtmDrqM/oSCwbjzVe/aRtmyR1AlSSyk2fvS1uyIW/s4KksBFZLPDXoTjspSzOePRqVPfB5NWjaeT/cRamvsFt3SG5H8XjprtivFSCuWEnY5adm5koyxvyDlnCHUlRRG1mMT7urRYxwRVlXrjIRcrag0WjkLverpZYpAuPHI56RlZrOnrpOiYQZu2eJRVNTNLPcwacZUXNI7XWKszQT3RTLCqWlptARTKKw009bcRFpmFtqh19Nctfxk/bQCAZBYv6WiogK3282MGTMoLi5mwYIFSJLExRdfzMiRI3s9nwiETzHRHcdVTy+jwXEMlc4oeyH2zBa37Vsn++tCt5BC06bFtP/tFbRpfUjRqNi//x2s4+biXluExpIpi1hp0rKxDZtFy/anEnvedU2+sUFy2751cX3IWZPviZQmhwIKr+JoZlr2zjNE7Ji0VjvQHcQrvI1TUuP8ju3j59FY/nBX5jki6qC19cU++W55Q8B94K3498X2IxtSSDXp2FPnj9vRtBpUqJAS7naeZ4/4dUaD4ihD8zR81iphXNzJOQP78fATotRHIBAIBILvEklVoNP6JHW/MJ31c5xvvyiLWfkO17Js2aMc+Ojv7Hl7FyGNDmO/cxXBrnPvGjrer0DyeVCnpGEZNC7GizcF83kj6PxHFSqDiVBHM5kT59O66xmsl0xKUOW2FJVaE3H/sOdiyLkA9yd/lYWwnHvXyFV40R7f2EpA76EaNJYMAo7E2WYp6CfzyrlyUBpVq25wHIu7dt7c/8d1W7bwcoGa3480cGuFh89apYgeizaNTRW7WPzwQubd/TA2tYf1U7QMzbOwp87PzI0+On3dnueBliPknjHwm/iZBT9gEum3nHXWWfK/Bw8ezKuvvnri5ztpIxN8LQJh6HNtMbnz1mAadxcrVizjZz+7lI7tZXgP1SCFgklLasI+NwPu2UK/OStob2miseELGtYVoTKYMF84htx5a+hzbTEqIhOTFAzQtGWJfF7voRrctdVkjL0Nlc6A73Bt97iaDye2OWo5TLDtWNxrtiEzkfwe0OrRpvdDCoVwbCuh9a0XCbU1JvQ2TiwO4UWlM0BYAhX0m7NCDoJ7e1+ovTEiuhUM4utsY+YGD28eDBIISbx5MMgtWzzM/Q8dniBcVx7/WtGwiNdYbFAcfXzuWf1pavoXe959QwTBAoFAIDglhMNhHnzwQWbMmMGsWbM4dOjQqR7Sd4YbbrxNsW7yHqrBWVWKOuBBY04nbej1OLaWUPf4NJpfKyNt6PV4Ptsvb/i7P95Ly86VhNxOqqo2097Win3CfIKtRyPB7s6V1D0+jc6a7WRPewBdZi5ZBfeQPnwW/eY8xYB7KsieVoT38w9Q6VOwXjIZld6E1mon7HNjGzIzIkK6cyV1JYW07FhJ2OXEPvHX5C0oJ2Ps7bg/3Yflp1dgHDAIlUZL+vBZZE8rQpeZS8bld+D65xuK79e4+VFQqdDa+irWb9Cd8Y6KbLXtWyc/r9IZ465d4OO38ZxXQMFaNzdu9tLoUZM6+BoyfrUJe8FvaO30AdDHrOblKVpGDdTK4qJrphnoq+3EV2ShfEInxg9f5fJRl347P7xA8BURGeHvAMmUo/fvWcUdd9wl++RpjYmN3aPG6c69a9CYbdhjynOaq5ajt+dhzh9B5vh5NLzye9QpqXG7l4Yf/YS2vWuQAj4aNxZjvWQStiEz0ab1SVzGk5qF5HMlVbNWG1IItTVB2I9t+I207npGnqRjj092fpXehBSWIOhNKCCR7H1qgxlD/3NJ/eId1hSaGPOCW6EaXTzayPR8LYv3+PGGtdxa4eGgUyLVAL/8uZ7p+VrePBiUe4QDIUkuhQ6ZLbz6aoVcCi0QCAQCwbdNbz6aP3QS+Qv/93/fLT/XErVNGjOO/fvfobFyGZIkyV6/PTVSmrYsIdjhINB8mB/dNJP04bM4uvpOWSU60HJYfm/bvnWRrG5GDkHnMayXTMQ2ZCbt+9bTXLVcXreY80fIG/veQzU0v1Yma7S07VtH2O2k84Nt8toNugWyNOZ0wgEfjqrlhNqbIvaSoSD2KfcScrUqss2RrPVWJL+bo6vvJPXS6QSaD+M9VIOjslSuPkx07RolE5o0Gyk9Ku+yCu6h6LdL8DpbGfsinGdXUzTMwMwLdQzN03DQKXUHxoVGflm9+1v53QWCr4oIhL8D9KYcHeuTF1VDVBi7V5ZiGzYL76EaOvZXkF2Y2AYpWgqjUqtkoaxoaU3rWy/SWbNd2dtbsZT2fetRp6Th2FqCfdKCuF7csNcVJ+zg6FKzNl8yiY73t4IELTuf7up3SaFhbRFaW1/ShsxEa7UTcrcrPIOjn20cMAjf0X8hqcH0k8GyMrXnk30EWupBo48bV9OWJYS9LjR17zDnP3TMrfIC4A1KvDTNJAtdvXkwiFkHYY2eurYAKTpo88KT7/lZ/LYfqwE6fFD2boDFb/tJtdnQjbwNjTWLBUUlACIYFggEAsEpoTcfTUFyf+FkIpZzZhfgO1x7HEXn7v7i2Eo5XWaO7BPcs9zZ0P+8rmRFLmmXzaC1+tk479+mLUtIH3Vz0iC8tfpZ0kf+FxpzOiqtHkflMjK7dFcApFCQuscjzh9RB42WnSsJOOpRp6QpNFkclaWgjlguhX1u2tvdXHvVGK6fdat8baLXrnrXNkqK7+0K8t+Ue4FTLBYs4U62XJuicOMA6GtRKSrpoirTAsF3GREIn0QSedediHpwb8rRsfTc6UzN7INZq6KlchnGFEucCBZ07yJGzykFfHHHeD7ZF28LMHlhxFJACiOFQwqvuvQRN6Exp9O4sRhz/sguNevD6DJzMOePpP3djV1lQ0WKm4L5/FGyqFfTliUASCE/IbefxvKHkfxedPZcMkbPwZw/Au+hGhpe+R2d/3gdQ/9z44P1zY917Yo2RtSr/V6sRg2d3iBPvufnlz/X88Ft3arPwbBETqqamzZ7eGqCkY+bA6z+QMXLhRExiN/v9rH6g4D8OKp6GBx2C+b8UZGLNXYuxY+tEIGwQCA4KbTE6/N8IzS4gnzh8H47Hyb4RunNRzMZarU6LgP4XUSj0Xzr45xz2zwefex3hFzOE1J01mV0B8Vpg2fQvP0psqcVxa2hml8rIxzwYBl0RbfgafWzNG54CCng61J99qK12mnZuZKUHuspy0+vwPXhTlp3P0c44AO1BvP5o2Tx0UgGuh6V3iSPJ5ptPvL0rQl1VBo3RHorVRoduXdvxHe4lhUrl2E0Grn8yqny95489VpWP7Octr0vYzqwhfUFaobmWTjvyc44f+HVBREx0UAYHh3TXW69py5EXp90rNbUuGvuDoYxmUxojEZMphBW44mHI6fib+RkcLqO+/uOCIRPEom861asWAYk34WM0ptydG8YjSY52J42cXDC0uOozL73UA2OquWoDKa4YwItiXuPQ50t8uOcO59T+PVKoSCSz43ro53YJyqzxRqzLYFy4UJadq4kffgseZe1ceMiCAXR2voScrfHfT9DTj6EAlh+egWdH2wja+r9ynNO+Q0tO1fSf8FG6h6fSh+zijmX6Nj0LzUHHGGefM9Pm1eibIKJ56aYKFjrRquGGy7UoVWreOo9PxuuSZEn9E3/CvJyYQ9LpSlQWPkidAXChpx86tcfxI8mbrxOb0gsNAUCQUJ6BrwebxBP13NnD0wnPaUTEswrPUk09xyP6NzU2eEhQ9z1T3t689FMRjgcpqOj45se2tfGarV+6+P8zyFjuGLM36jcWp7QklFjyehWdO4SA40KZ6WcMwTH1seTBtA//dl/8lHNdgBctdXYJ/5aWf12xkWRDf22RsIBX1yFXaizhdTB10TeO35eZN0UPVdMCXRP1eqgM17DJept3G/OCupKCmUFaS6fy6qnl/OfQ8Yojr/lF/N4puR+Xr7GIK+LDjqlhI4bn7VKZJg19LWo5Jay6zYHcYZDPPvsX+KSB340eDwgBQJ4PB46Aif+e52Kv5GTwek67u874pZ4kkjW5/vS808fNxBO1NMS62EXpbdgO+jpJHP0rfFqhFuWEHa3dSs5Gy1xE2bsbmIU3+FatLa+hP0eQEocYNtzSb10ury7qbX1JX3ETTi2lvSamY4+lvwe8rp2JB2VpaA3kTH2dlnZMOoJnD58Fu1/eyXxOR31HF11BxY9zLlEx8sfBpRy/xs8PPleOwPSVLj8cGa6iiffC/BiTYB2n9JeKZmlUltzE2kx37tv3pkcbDHF/Y5ioSkQCGLpGfyePTBd/rfJZMLjiWRPBmZ4AE2vHsLRADjR3HM8xNz0/aI3H03BV+O8Cy5ix5vbyehhyRj2usm4/DYsF4yWe4NDrtauzG7EkUOlNybROjHSP+cM+uecQVXV5rjWtazJCyMZWq0Bld4U56oRCXyL8Xyyj1B7k7xucn2wFfvU7gy0wrEj4EGTmp3U7kmlN+Lcu0bWloHuVrxYdu/aysYX/kCnN8TcKq/cB5zMX/jcs/rT1BFg2hY/7W1tEfuk4bNIM2eKKjrBdxpxWzxJ9NbneyJE+zKsVisVm9by0vNP88Ti+xQl1r0F21qTBa3VLqsRBpoPo03rAwEfFW98KAfR2nOG0/6/FZHgNeiPlN+cPzqppYDns/0Encdo3LgI6yWT5dLm5qrl2IbPRmNOR52SRqitkX5zVqDSaGnbt65XUa/ux7nyjmSkZOdhua+5+aTQ0U0AACAASURBVLUywn4P6aNuBpD7c0KuVlmQQm1KRW22kXnlXBrXFbHpX0FWFygzunMu0fHUe37q2iOCWNdeoON/RkTKpa8r9/D73T4WjY4sRpNN8KlpaUihIL7DtbRvL2P2L+5K6hUsFpoCgQC6g+Af51hQ6SL6BOkpndiMkYDWZAKPJ9Jb11sADJEg2OkN0eq2fGWfcjE3fX9I5KP5feKrtpl9rc9q+ILsq34bb8m4qVi2gIz2Bqs0WmWPbuk1OCpLsU+YrwiipXCYqte28Ou7H6Rqy7qk1kZ9rnqQhrVFSRw03ARa3LJoqNqQQqiHzSUgi3IBhL2dGPMuTKjhYv1ZAZ012zGfM0R+b89WvN27trLmyQd5sUDL0Dyrog+4aJiBmzZ7eG5KrL+wl0kzRvDnZ9eS++tybD2qB+vXH/w6P5FA8I0ibo0niRPt8z0eO17blDTr21uwPb5gBq9XLMU+eSE/uukPkUmvYilXToj0fERvIstKHqLPVQ90GbHfLo/X1f+8SB+w8xg6e24kCP6/9+LEH9r3rY+IXQ29Ho05HUdlKekjblIEv4m8+poqlmIZNE4OKJsqliom4kjJjlf+d9B5LGLB1HUDShs8g6bNj6HS6eWbzdFVd5B55VxCrlYsBg0HHEFFRnfNhwFe/jDAhmuUog7nZ2mYeaGOlwtNTFvnZsxALUPzNEw9V8t15R5Fj/DMcg/ozdSXFJLd/wzuTJCpFwgEglhaYkqeAfqYtaQYOojN+qZo1YSOEwDHEg2CRUArSOSj+X2htzXQybj3xgbZ1nQ7vpCEbfx8pHUPEOxwcHT1nXKPbuql0wn53HRsL4NxcxW9wVF8h2shFMA2bJaiv9c2bBbNlaWoU1IpXfI70BoU73XV7sb59ksgSbTsXInGkoFz7xo8n+6Tz2E6ezAacwZhv4dAcz2NGxch+TxJq/g0lgz6375aTlaknPXzrjHVozaYybj8Dsz5IzAN+CnNr5XJ67GO7WXM+sVd8rz1l6cf4y8F2rg+4LlVXsrGG2n3q5m2WUt7ewdpmVlIF05ix5vvkHvGwITjEl7Cgu8y4pZ6kviqfb49Wf2nsqRZ396C7TvmFQGwffMjBD2daE0WLsi/gP3736FgzCB5VzXkdUdsk5rrFSXSEUl+L6mDr5HtAeLUEycvjPQZa3U0Vy5DpdUjocb59kuRrHFX+bXaaEZj6xszaRuR/F463q+g/W/r0WXkYhk0DndtNa7+52HOHxGZxNOy5e+kNlmRgkFFH07rm3+Wvf4g4iUc7HDQtucvmC8uxPr+ekVGt/htX1yGODqZR6X+233I9koWPXjTz6KwsoO25kYsBg3Dr5zB7b964Gv8ZQgEgh8iP86JiBn1MXffZo+X+RUIfuj0tgb6uoFwz/ayo6vuIHN85LM0lgycb78Yl9VVafWMHDaS/XtWda2blpA1OUb1uWIJKp0BrdVOv1uekj/Le6gm4vs79na5fSy6ngl2OOI+q2HDooTuHWGfmz7T/0dph+Rzx1XpOSpLkUJB3B/vle0yW3aupN8tT0Wy1iWFCiumoPMY9SWF9Mk9k1m/uIvLRk+U56xjjlaG5ilFnYbmaahtClOw1o3x8vmYLxiDres1KRSk/olXWbliScRZY2z3Oti1s4yHihd8rd9NIPgmEYHwSeJE+3yPR0P9Z0mzvr++/5F4+6SNxaCCyaMvRGuyMG7cJO6YV5Swn7hs2SOoU9LIuPx2eVJt3FiM5I9YGyFBZ812TAN+qrAHiB1HqL2JnHu2RMS3tj0BSOiyBhAOeOMmcH3fn2DrUlpEn4J90gJFEG8a8FNadqyMZJa3lWAbfiPeQzU0VSwh7OlAY04n5PfIitLRMUTRZebQtncNmePn4f5kD+EQioxusp7fA44wECl7NuuhbLyRmeVe2n/0H4SOfozT5aRPzpnfaDmWQCAQCAQCJb2tgb4u0faykKuVL577lVJQSq2J79GdMB/H1hL2739HTnZozxkeSSa01KPS6tHrjfgCvrgAOdo+FhWoypw4H+fu52lYW4RKb8T6swJ5HIHmw6j0poTuHbEew+7a6jg3jvZ969HZc2U3j1i7zFjHkJ6taWn2vry0fgcenQmNFn5kj7SIvV6xEbNelbhNzGbD7QthtmYprms06xvtAy5+bAX16w+Se8ZAHipeIPqDBd9pRCB8EknmXfdl6JN7ZtKs78gxEznw0d/lrK9Ko0NlNCsC0NcrlgKwf/87il3VkKuVQDCA5HfTsnMlxrxBeOtqIkGwzoT5vBF0/r0KywWjaNm5EpVW36sCddOWJajUGuyTFtC0aXG8ovPkhTRtWkzI1YpKpSbY2ZJEQKuepi2PIQV8NG8rRaU3dSlC34vvcC2Nmx+FUKRep2c5UNrgGTgqHifU0YTqn1VUXJfCsU5JzvCmGkg4mZ9nV/PmwWDEUikEU9d5kM6fQPbld+A9VAN7VrHqhS1xv8232bckEAgEAsEPjd7WQF+XxiOfk9FVRZY5fh4tO1bKnxUVo4rFkJNPyNVKo6tVodGSPnwWrtrdtO5+DltXVrdp86OyWJUuMxfb8NmyDSQ6E63VzyrWao5tpXQG/WRN+Q2GnHzqSqYlVp5uawBI7HHcVaXX75ancNXulqv9jq6+E9PZg7sdQyqWYo5pTXNUlqIJBdi6fRu3//etGFVN2Iweyl/dQsnipWh+djUzN7zCmqtirCS3hNEN+S+Clctw7SxLmvWdPn2yCHwFpxXq4x8i+Da55da5dGwvw3uoBikUxHuoho7tZdxw421U79pG9dvVZE65j7y7N4JWJ+8gyqJTkxeyfftWGo4clCfVqFF79rQi8hZsJGPs7bg+3ovp7MHkLdhIduEDeD57T84W97vlKbmMJ3YcTRVLuwLXJUhBH6Gu4DbsdSWcwMNeV8Qg/sq5stBDLFHFalCRfdWD5N0dGUug8SDuj/fi/uSvEA7JNxbjgEGKMWnM6ag0OrR7nqTDF5H0n3mhjo/utBB6MJU/XGlk5gYPbx4MEghJvHkwyHXlHmqbwtxa4cHtl7CnqOjwSRj658vjbuyx81y9axvXTx9NaekjMHQOuQvKYegcVqxYRvWubd/Y34JAIBAIBD8kelsDfV2y+58hV5EZBwwi7bIZ8poi2v8bi+9wLdq0PmT3P4PGI58r1jmt1c+iUmtpWPcAXzz3K8znDsM4YBBqUxoZY28n5ZwheA/V0LjpUVRqVfxabeJ81IYU+blkayRtWh+A5FV6bY207FiJ860XyLj8dvLujqzxOmu2E2iuhz2r0BPCfeAt6koKIzaWI24iffI9lL/0NAA2Y0S74LHHnsQ8di624bNxqNOZtkWPobiTwkoLwWG/RGPNInfgmZQUL8Dw7mrqnyjE8O5qSkTWV3AaIzLC3zEuv3IqXq83YYn1nNkFiiyvlEA50JCTT9DTiTrGLzjZTmKsr699wnwayxdxaMlkdBm5qPRGLIPGyf52Kp0BSZIinxvyY/1ZAZ5P9kX6eY3mJDL9JsUNp6eAVqTfWE9WwT2KsWWOn4dj2xNI4ZCiDKi5ajkaa2ZXL7Ibo1aFVSvR2A5WAwoFaICcVDWdfomr1rtp80Femkr+DgBqFdx4kY4/fpiC860XgIhlkzXdzpzZBTQe+ZzUzD50drQTBjmbnjZ4RqTX5iT1LQkEAoFAIOh9DfR1ueHG2ygpvldeN5nzR+CoeDyhbkqsfZJHHSI1s4+8znHV7kYKBRWewM1Vy0kbej2eT9+BPauoP/I5aoMJtdZIyNXRa7YXuirceqg8N1UsRQr6FYF6IptLV211wqq8jqoSVr2whYIxg8hd8AKqnmrOPTb96z8/SG5hZJzpI2+JJFBmFMdlfkXWVyCjklBrTm/tCxEIfwdJVmLdUzW6twBUUmtlOf9Ac31SX19X7W7Zjkil1ZM5YT5aq52GtUXoMvqjAoWZe097peaq5RhyL4i7gTRVLEXyexQ3HEC+4WjSskkfPjup53DY20n2VQ/GBciNG4tJ/dkkVP/YSKouyHNTuhWhryuPyPvH2iMFw/BfF+komxDx3gyEJAyLOjBqVVx7gY7VH6rRDbuFTHMmza+VoQp4UGt1mIbOkXurOyqWYh00TmEdBZByzpCT0rckEAgEAoEgwsloM0t23lVPL1Osm3T2XNlBw1W7W7H5r9IZybj8NrRWO60VS/BWlWIbP5+2v65LuIHf/FoZqRnd/bMWSyptjmNytjcuiO3K9kJkjeR31NG0aTFhnwu1wYykUmO9ZJKs/Ny0ZQlZBffEBd/N20oTrqMa2lpk/Rjn3jVySXfbvnUEHPWo9CYuzs0kd+CZPPCbOxSqz9E1W9RNJO/MM0W/ryAOSdLilzJO9TC+FqI0+jQiqhodxZw/Mq582VGxFCngJ3vKvaSPuCnS76szJiy50aRmRcppxt5O3oJysq96kLY9f4n09RpS5BIiz6f7ZBEJ98d7adm5kmB7I67aakxn/Zxgy1HCLieN5Q9T9/g0GssXEfZ5UOkMPcY7gozLb0dr64taZ8CcP0L2B+45NingS+qpp/q0mkxDkOemRBShdRoVowZqebnQRNm7fgyLOpi6zs20c7VUXZ/ChgNB5lZGguQ9dSGsBqh1SDx5wEZw2C8x54+SVRRNJhO28fMVJUxZkxfi+XSf/Dhz/DzZLupk9C0JBAKBQCD45plz212K0mvT2YNxdK2jUs4Zgn38PDRmG9b/N4XcuS9huWB0pC948j0YNCroUo9OmOF1HsMXkuQWKuPYuagNKZh+MpjmquVxrWZhn1vxnKu2GsvFE9GmZmPOHwlBP+1/ewVAdtVofq1MLnG2DZ+N1mqXkyKxRLPFusxcMqfch6tmO43li2je+QwhdzvqlDSyCx8gb8FG/P9xCwuKSrh81H/i2lmmaD8zauHpp5fywXuVIggWfC8RGeHTiJ4WTSk/uQz3P9/EsamYkNeN1mThinGTqNy8tkvVMOJFJ/m9CUtukCTsCXY1W3auRPK5CXaVXkd7U6K9xpk9MsMp+SORDgToN2dFpJxo6+NIoRCWi65MvoNZGfEFTBs8I6ERvSYtO4lYVy5tzfW0IyVUhO70Q26aikfHGJl5oQ6AvxSaKFzvJs2o4i//0qJPyyZ79F1x5/7RGWdx7NBn2JJkz5WP67+SPZZAIBAIBIJTQyKHj6Fjr2R/Vzlzdv8zCLmcdH6wjfZ961EZUgAVks+FSmcgEPArWs+i+A7XojGmyBvpEFlTWS6ZRGfN61gGXdGtNq03YcwbRMpPLpMzrmpDCmGfG/eBt9DYfkTnP99ECvrQWLMIudsJu9tQGVKQwjr6zFikEN0y54/sda0V1Y9peOX3aMypqNRaMq/sYVE1di473oz0+wrVZ8EPCREIn0YkmsDvuvtBRQlR9a5tvL7zNdkiSbYyyh6oMHs39DsHz6fvJC2Z1tr6IgX9suy+73Btwl7jaLly5rg7cX+8l7Z96yIZZZ2J9n3rQaOPTPRtDRGT+eGz0ZjT0ab1kXccwz43jq0lhFytkecDfsznDYvrKW6qWIJl0BWoP/WSGWpKqAht0cOiUQY5CAZkv+Cyd/10+v3k9jHQsO0xmPgbRe/LgvuLeOqJJxIH4D2sBzSGlJPWtyQQCAQCgeDbobfS6xXLi3mttUVhheSoLMU29hdorfZI33DQHd9PXLGUkDdet8U2ZCbt+9bTsb+iW/izy7WjubIUlc7EhCnXyraXTy0rxuf4XKGP4qgsJXPifAItR2h/b5PsS6wxpmC+eBLpw2fh/vRvCdda0bWLIScflVqFfcJ8GtY9kHDtV7f2MwA+eK8y4bXZ8GoFpUtK+fjgUc4Z2I/598znKhEkC05zRCD8HSWZVU/PCbx61zZZ2Cm7/xl4vR7sMV50IVcrap0Rz6fvoLX1JXPCXWit9ojwVFo2zr1r8Hy6Tw6QTWcPRpvWh5C7HVTQVLG0WzSrvTFpuTIQly12VC1Hnz0Q39GP43YwpVCAusenyX566cNnyef0HqqRy36iu6VaW19Szr6Mzn+8jvWn4zi2f4PCM3hPXYiZ5T4CYRU3bfZy/y4fWg3UtUkMtKkwaWHTjGg/sY8bK4J43lhGvcNB7hkDebB4IRePnsaxRhcvPtPDq7mH9YCzqpS7FjwogmCBQCAQCL7jJFpPAQnXWK+/voWsqUVxfsItO1fS75anMJ8/ms6a7VgGjYvJ8KYg+dyk2fsm2UjPJdBc3/W/keqytMEzSDlnCHUl06h+u5rzLriIAx/9HW8wSPa0B5J+vmnAT/HuLOOl9Tuo3rWNFSuW4R3wU2zDZ+N8+0XFWivqZRwdR7TlLJrcSCS8taCoBCAuC7zh1QoWP/Agfx4PQ2da2FPXys0PPAgggmHBaY0IhL+DRCc367i5smDTihWRUuKeQXDP49rWFpHWwzZJkVXdsgSA9FE3075/C5012+NErsKeTghHvHslnxvPJ/si4hH6xOVA2rS+CbPF9vHzcFQtB0misfxhpIAXXWYu6SNvkv31Gjc8hG3ITMX3j2alNeZ0wgEPprMvJbvwAQA8//cunf94HbsuzC0X62TPYIseVMC2mSkcbg/zwJs+/lzQHSTP3uThWKck9xM/Pxl+uVfHxwcOAOBHw8EWuGz0RCwaZdb9irFXsn//O9SXvEJ2/zP47/++WwTBAoFAIBB8x0m0Tipb9ggqrQ7b+Plxa6xEWd3Y9ijPJ/tkK6ToBr73UA2NG4vpaHPS0aNEuWnzY6jUkTYuKRSQkxFNW5bQsutPoNLi8/spKb43InQa8CT+fEfEHzgqbFq9a5uiSrDlyOdY0u14dpbR0BwptbZcPEm2cerYUYbZlonvcC1pgyMuHub8Eag+raatuRGLQYMp/0pSfjKE4sdWxAXCpUtK+fN45Cq8UQO1/Hl8kF8uKRWBsOC0RgTC30Fijduhq38jgVVPouO0tu4dyYS2SQX30Lz5EVoql4HOSHahcucxa/JCGjcuQq21IklhpFCAQEs9OnuknCdRubIUCiH5OpN63PW5tpiGdRFRhlj5fkNOPlLAl1j5WmugZcdKwq42gi1HZaXDUGczFh2suTYilLVodOQ9OU908OK0yHMXPNUpC2lBZMJ+YaqJuVVeuWR6aJ6Gj186mvD6f1OKlQKBQCAQCL49ouukkKuVL577VSSQ1JuwDroi4RorKvKZrD0q0JJYKEvyucm+tphgh0OuZFOZrKh1BoUGSjRLm1VwD46tJahNFuyTFsivN24sVny+q3Y3Lbv+hDol4k9sSJAcGTlmIi2R3AU/zrEAsH3rZp55soz6klfoO+BMFhYV8e+a91mzppiw341ab8b44SusKTQxNM8acdrYsovgj35C/ecH467jxwePMnSmRfFcb+uoE+Xfhzu/1vsFgq+LUI0+xURLmwvGDGLO7AJ2vLYpzrgdIhNtYw+rnkTHpQ2ZSdOWJXgP1SQ3YPe62LKrRmFvFPu65POQcs5loILsaUXkLYgYtLs+3ovKZKVxYzF1JdO6sr0geTtR6UxJVQuNAwYlVYfWmNPj1BSbq5aTeeV/k3H57XJJUVTd2j7hLlz+eKGsox3dzx1whBMKaR1whOXHe+pCnDOwX6KfBIj/Xap3bUt6rEAgEAgEglNLovt245HPCXY4lA4Z04pw1Vbjqt0tvze6xjIajTgqS5VuHJWlpF46He+hGlT6lF7XOpYLRtP/tj/R59piCIewT1C6UERdJww5+YQ6W+TscvR16yWTZDeQzo/eoHX3c6j1JtmuST6uK3CPJRoEJ+LfNe+zeWsVWV1rOqtJy5rCHs4bBWoCe58l94yBce8/Z2A/9tQp/WL31IX4ycB++NF86f+c3hBfOLwAZIiUnOAUIv78TiGJSnZKlz2qMG6PksiqJ2qnFHuc1mrHoArTWP4wKq2+1/Oo9MbE2VhDCu6P/0rW5HsSZost54/G0P88nG+9gL2rBMi5d028l/CWJaSPuhlALsVR9BBvKyV99C0AspCXSqsnY9ydaMzpOCpLMZ8/muB+h5zZblt9M2emq+KEssx65OfOs6sTCmkNtKkIhCT21IW4uQruXzT/hH+XRKXpAoFAIBAITj3J7tvWdLtsBdmzdatl50rM+SMibWRvv4QkSegNJnxtTll4SmNOJ+TpoHlbKSqdETTaOKeL2LVOlGCHA8mXvMzad7gWlc6YVFyrZUfEpjL7qgeTilvVH/lc0f+cltUXwmGczQ1yaXTu1Y/jO1zLmrXFit7n9rY2hubFZ3jbnU6WPBZpRXv11YqIgvTnB8m027mxIsjzk5Fbzm6uhJvuv5eDLaYv/Xt94fDS2eERQbDglCP+BE8hCUugL5+LZ/syOraXKQSbEln19LRT8h2upbViCeEwqI0WzOePjrdN2rKE+fPvi2Q4pXCc5H5TxVIkv59Qkglc8nvw1tXgratR3FiivTKNG4uRfG5UeiNSKIjWagciHsK+IwdiXjcpTNvlnuHyh2neVoomNQvz+aNx11YrembaHI1IBhjzgpsz01X8zwgDOalqwmG4abOH56aY+M0QvfxvWUhrg4c2r4RhUQd5fdMpWlSUtK/lREvTBQKBQCAQnHqS3be9O8sIthxLEpDW0/nRGzjfflER2HqrSjGoQ3QAdlsaDa4W8u7ubu1q2bGSpk2LCXtdkbVOsHutA136LG+/qGhVi+I7XIvalBpZCwW8HF11B7ZhNygCcoBge+Nxxa3UBhOlpY+QVXCPHPw7KkvJnDA/0odcsRT/F58Q6mwm3KP3OS0ziz11nXEJg7y+6UyfPplXX61gQVEJ5rFzyS3sWh9ue4z/2q6mvtHJj8/ox03338vAC0bx6cHWr/SbiSBY8F1A/BmeQhqPfE5ugsm5oaWJ1IwsGjdEBKbS7H0TWvUkslMymUy4gmDv8ojT2/O6sq0RZUOr2QxA2bJH0JgzCDqPdQenBhNqVKh0OjQp9qQ+voHmekCVdCfTctF4PJ+9h77PWXKWONjhwP3vdxSWAE0VS2h960VsQ2bKE7gU8EfKoVsO4/l0H7bhs2n76zp8h2txf7KHNANkpqjo8EsAzK3yolFBIARtXolbKzwcdEpkpcDUdR46fBIWgwb1JVeTPeS6SI/OzjKkr/C71PcoTRcIBAKBQHDqSXbfbmxuSKrmrDVaaNm+guyrfqsIoG3j58OeVfzl1TcAmDZxsPx+V+1uPP/3HllT71fYGzVtfoysKRFLRufbL2GfMJ+QqzWuEq6xvBiVTh9nj+St/yeez97DfP5oPJ/sk/2GY8WtFBV1FUuR1Dq5ZDo69liFacugcXTWvE7W5Hto2bFScQ20g2dx3eYyXp4Sk+GtgqJFRQAUP7YC89geGwsTf0P43dX8s/5v1NZpRUZX8L1A/AmfQhKVNjv3rkFjtmEadxe2mGxwMnoKOxWMGYQEcpBqzh+BOX8EUihI3ePTGHplAU8seRCVzkTWlXMVE7Hk6eSKCVOo3LwWY/5ImiqWKLz0mquWk5I/kuB+B0jhxIbyadn4Dn8k98VEd06lUJDsqx7sUWp9D43lD9P+t1civnfDZtG8rZSMy29XnNfvqMNRsRS930l6ioo/Te7O9N602UOTSyLlP69Bl9Efx95nkSQnXm0qqkFXwv4KUqYpb3KMnZtQFbG33yVRabpAIBAIBIJTT2/37UTVcx3by5j36wd4YvF9xy07Dno6adxYjPWSSXg+2RdfZj1hPo6q5XKLF5IU8e2NZpDl1i8DppQUrOMXxL2/sfxhrD8rwF1bLQe8sS1naUOv7xbh6rKdbP/bKyegcB0JlNMuUwbTGnMmzrCJm3doOHSsBZtJQ5snROmSUgDqPz9IbmGC67L+IK1uC50drSIIFnwv+E6IZe3YsYMFCxac6mF869xw4210bC9TiDJ0frBV9gHuTRQhGdn9z0Cb1idOzMG5dw0qvZHKzWtBrY0TXrBPmI+ExP79Eb9h7+cfYOh3riyM1bJjJSn5I+mseR1QgVZP4+ZHOfL0rRxaUsCRp2+lcWMxKWf9XCHSlXH57eTOWyuX+MQSKbX2MuCeLfS75Sm0VjvqlFQcXUIR0WvirXkdvVpCo5JkNeiouMNzU0xo1GAbPhvzBWOw3fYS2dcuxqUy0/nPN5H83sQ3uQSqiL39Lh3by2TvQcEPhx/q3CQQCASnE73dt0eOmRhpLduzivqSQtizSq6yiwbQsfgO12JNt0eC56FzyLt7I9nTiuis2U6gObFqdKi9iX63PEWfGYvkTC5EkhHR57P7/AhPe2vStVBskK3SaEkfPgvLoHE0li+iuTISoOrNaUh+L7YhM5OKkCZSuDbnj8A2fDYtO1ZS9/g0mjYVc/01k3josYcZYE9hw3QD3iIrTw5pZfEDD5Jptyc8d04CIS2B4HTmlAfCixYtoqSkhHA4fPyDv2ckmpzDSXpzeypGJ+OGG29D8rTLqoNSKEjrWy/SWbOd7MLfknf3RiR/kv7fgI+GwwdJGzKToPMYWVPuJXPcnd2lyp/sI+xqAymE5OlApVKTeeVc8haUk3nlXNR6I64Db6NJzYqbQKOWBLFExCIM8k2rsbwYVThIyO2kefMj1D0+Dc/2Zai0OlIn3oPLT0I1aJdfeQ0MOfkEncdIH3GT3KPT83NzB+Sic9XL/+ld9fLrg0Ykv2kKfjj8kOcmwckhPaWTFEMHKYYO9DHzjc5Vj6rtoOJxb/8JBILeSRbsAsyZXcATi+8D4Nf3P8KqF7bI9/NkAbRKrZF7jqMJg6zJC1EbzYnXMlo9dctm0PDq7zEOGBTnhuGoWMoNN96WNPDWGFMSWjPZhsxECnhQm9IIdjQz96776JMzUFEyrficbSWEvW4OLZmMSmfCuXeNfC5z/oiIG4c9l6ypRex482889tAjsj9wNMFw/blBgh0OGtfej/PpG3B9tAvvoRpcO8u49ze/POm/nUBwKjnlhQ2XXHIJY8eOZd26dad6KKeEnqXNt9409WuVrfELkgAAIABJREFU5Y4cM5FVTy9DOnt4dzlOD79gXWZu4rJmSwaqkB+t1d5V4lwrl1ZD1DR+EdnTHqBlx0pFCbNxwCDsExfQ/FoZUjAQp6ooSVLcc46uHc66kkLUplTUOj32GNXpyM0IbF03I6vFlFAN2qIHV+2bmPNHyd9FZ8+NlISHQ3GCYK7ty3j49mmEPu1eYOrOzmVghgdI59ODrcJLWPCDn5sEX42on2dGmo/s0FF0rm7Lkdg5J2QyEfJ4jns+jdYHF5xxsocpEHzv6HnfPhEHiERaK3fccRdPLL6PtAQJg7DXhaNiqWKt4qgsJWPcnbJAlcacgXn4MBxVywm1NaLSGZHCkXkgUZl205YlGDQafF3WTPEexrlkjL2dxvKH5fFGv5eiZNpoBknqocWyFEDWYon6GEfLnFVIDL22Wz16zYcBXv4wQPnV0RY0PzM3LCNkzqKk+B4KphdwsKX7+kYVq6Ml6GLdJDjd+NYC4VdeeYXnn39e8dzixYuZMGEC77zzzgmfR61WY7Vav/TnazSar/S+b5tbb5tHyROL4fKYXpYdZcy/694THn97cwO5N82UlZwPPTZZqRY4eEZCNen0UTfTvK2Upk3FhH1uuScmOoE2VSzFeslkjAMGEWg5TLDDwdHVdxJoPowuM4fUS6cTbGsASQKNjsbyRUgBDxprFqjUhAM+2ZJAm9aHsN/bffPYWIx9WpEisPacM5y2fevlm5F+1J3M3PAEa67q7hGetdHDmIEa3thRSvPWEqwGNR3eMKlpqbhq30RrtZORosO47xnq6urIy8vj0YW3c+2EyxXXTH3k36gHmjCZzsNk8mI1fnt7RKfL3+bpMs4vy6mem+D0ubanwzhP9Rg93iBnD0wnLfghlqOfI0nd1iKS/cfyv9VqDUZzKNEpFKhcR1AHGuhj68dhk+lbnZvg1F/PE+V0GafgmyFRUHaiDhCJNr5fev7phEFpn5yB8rnrDh9Ea+tL+oib5IRBxGayGGPu+aiAPtcWE+xw0LZ3DSXF95Jm78vIYSOpriqhoa0l8v5RN6O12ml89fdx2ixNW5YgSVLEjsnvlccbHWNz1xjsk++mbd86MsYqExRZkxfSWL6I9r+tR5eZG2kj63LpyD1jIFa1jz11LXKCofhtH6sLTPLjUQO1rLnKxC/36pg+fTLRAry/vrGNF58RNpOCb59wOMzvfvc7Pv74Y/R6PYsWLWLAgAFxx/32t78lLS2Nu+++u9fzfWt31Kuvvpqrr776a58nHA7T0dHxpd9ntVq/0vu+bcaMK8Dtdit3J2+/i/8cMqbX8cfeBDRdpTvRyVDd47E5fwR+R53S6kiCQMsRNGabYqezqWIJ7fvWo7X1JexyYhsyEwCNJSPOcsBRWYralIoU9GP92WTCng5cB3YTam+KfIbXRSgYAElCCvhQ6ww0Vy5DpdUjBZU9xK7a3bhqqxX2A+b8UdRVlDC3yssBR5jz7Gqmnatl26dBNl3THRzfssXDdRd6Wf1WGc6ggUeumsTU/3ex4nod271P8VgbdJLS/wyMmiY8ngAdgZPyc54Qp8vf5ukyzi/LqZ6b4PS5tqfDOE/1GDUmE5ne97Ec+TeN++sIam0xr9bJ/zKlmPC4j58R1gadpAWcpFzQB41W9a1/t1N9PU+U02WcgpNPssyvu/UYeV/SASK6lmo4chD1pmIsF3cnA6I2ltHAefLoC+k3Z4UsihU9v+Rz43z7JTLHzyPkaqVtz18Uis+vVywl7HPT59piRaCtsWSQct5wOZOsTklDpdUT7miiZefToO7+nOgYCsYMIm3ITNr2rSPgqKdlx0rSLpshB+aRtjcPakMKGWNvx5CTL5c5P1S8AJPJxM0LFvLn8UGG5mk44AgnbEH7+KWjiufKXxI2k4JTw86dO/H7/axbt46///3vPProo6xYsUJxzNq1a/nkk0/4+c9/ftzznfLSaEE8X7Yst+dNwLl3jaJ0x5B7gaw8GJ2IO//xOqAmc2LEb86xrYTO9yvImvZAj93Ee2jc8DD95qzgi+d+1R1QqzXYEygnNpYvQm1KpeODSlRqTVyJjmXQODlTDZFya8emYsIYFcF627512LtuIrFKh6k2G2Xj/fJu5QVPdcbtXq4uMDG3ysvLU4z8V6WayZOu4nh5F+3h99Ed9yiBQCA4Pma9hlDQQFBrI9T3zITHqFOthNpPIHA79tlJHp1A8P0iWebXv/mRL9VqFruWyouxKWrft54+OQPlIDgaLEe1T3qeX6U3EXRGvIu/eO5X8SrTkxfSWP4wDeseQJeZQ9rgSOAabGvANmQmnk/3YR58jUJBOjqW6l3bFOtDa7o9LinRXLUciCQ9In7DZiSfC/27q6lff5DcMwYyacpoih9bQf3nB8m027luq4uGlg6sBhK2oJ0zsJ/iWjmEzaQAkADftyw3tX//foYNGwbARRddxEcffaR4/YMPPuAf//gHM2bM4LPPjn//FIHw94CeN4FooBktTdZl5hJ2OWMM4E0YBwwiu/AB+Rz2iQtoWFuURETLG+dlF2pvSqJ86CGs0YEUJqvgvvgSnY3FmAb8VGnbpNZhPvcyRbAeVWaMsx9Qa7huE7w8lV53L6PP1ze3n9yLLRAIBAKB4DtDMg/hoKczYj/ZwzYpKqLVk0QBtX3yQtizilUvbAGUwXJGhyNO+6SpYmmkfezwP/EdrlW4aMSOTQp4yVuwUV4HtVY/i0qrl9+DlMCmafLCuIyrSq3BPkE55szx82jZsRKNOR1H1XLCXhc5A3/Mu+9VoSfEq69WsKCoBPPYueQWRpInrTXbyb62iFBHE9dtfjLOX/j+RfMV38EubCYFQFhS4Q/oTvp5W1pamDNnjvx4xowZzJgxA4DOzk4slu6+do1GQzAYRKvV0tjYyB//+Ef++Mc/UlVVdUKf9Z0IhC+99FIuvfTSUz2M05ZENwHbkJm071tP3t0bUWm0HF19p9w7cmhJAVlT7lUcb8jJR6UzJt3dbNq4iLDfg8aciWNriTxhJzpWY0gh2HYsSaDspnHDw0hBX7d3cOUyfIc/wjJonBzwqg3mmJLoEXJPS8vOlQQHX820LatpdzqT7l6eZ1dHdjH7Z5/MSy34gSHmJoFAIPhuk8xDOLafN1YIK1nFXbKAOjbL+acVJfjDGjmbm/LjS2WxKrXRjDl/JOmj51D3+DQ6tpfJdpaxY3PuXYPaYKaupBBdZg7m80fjPvAWGePuxFFZijatT0IF6UQZ1/bmhoSiXoHmelp2rsScP5LOD7by67t/Lb9e/NgKzGO7g2fPp/tk204Al1otr7HOPas/9y+az1XTJys+o/CG23jxmXhv5mSbDALBlyEjI4Py8vKEr1ksFlwul/w4HA6j1UZigNdee43W1lZ+8Ytf0NTUhNfr5cwzz6SwsDDpZ30nAmHB1yPZTUBjTOHoqjsItjWgsWTIO5e6jJyEx1ut1oRqiGi0ZE25V1Gec/HFP+dfCXZaJb+HYMCLxpqV8DO0aX1RaXX0u+UpIFIarcvMIdB8mB/FCHy5anfLgl5RoYmg8xhqkwV/9Uo6OlxYrWakYJibt4b586Rgjx5hHTdvDXPvrTd9Oz+CQCAQCASCb51ESsw9+3lPhGRrqWiWs3rXNjq9foULRXPV8ohyc+UycuetxVW7m6Or7gBApwbJ51I4Vzj3rqGzZjtZU+/vXlN19QRbLhiNSq2hZdefZC/i42Vck405qjTdVLGEqVddzZRphUCkFaP+84PkFnYHzz2z1ub8UUjnDKP9iUL2vPtGwmt12eiJWDTxatuiP1jwTXPJJZfw5ptvMmHCBP7+97/zk5/8RH5t9uzZzJ49G4Dy8nI+++yzXoNg+A74CAu+Pol88JxVpahUGtnn1z7x14QDPpo2LiLQXI8jxmc46pt36x0LCLmcEcP1kkJadq6MSPFPuVfhpWefvJCjx75I6NmXmpEFGh0hV5vCy9h7qIamiqUEO1swnT242/OuspS0wTPijOEjWeCRNLzyO1rf/DOZV87FPvEusvU+Nk2T8D1gZdM0CZs+wLCfj+COnWaMxR1c9YqPz50Sr/5fGvNu+AWFo0afwl9GIBAIBALBN0kyD+Evq7XicXfStGVJ3NrohhtvAyJBX1bBPYr1UOb4ebTtXYM2rQ+dH71B6+7nIuuuuzdiHDsXlcFM/z5ZNJYvimSJ91dgGTROuaYaPw+10QxE1j45dz6H5PP0OpYoidZ/TRVLCTTX07SpmKkFBdz9u2L5+A2vVpBmVFNfMpW21Tfjqn0zbv0FkWA694yBx73uq17YwpZdNQpvZoHgm+Tyyy9Hr9dz7bXX8sgjj3DfffdRUVHxla0uRUb4e0AiHzyDRoWpK4CFSN9I9pR7cWwqZkHRoxz46O9s3/wIQU8nWpOFceMmMXLMRF56/mkYOkd+36ElBYnLc+o/S+jZ5wtJaC0ZAKSc1+1lrMvMwTJoHK6aHXg+3Uf739ajMaSg0ejQmNNJvXR6XK+Nq7YajcmKfdICjAMG0bb6ZtZP0SqEsZ6fAnfsfJ9ty5SKcQKBQCAQCH4YfFmR0Vi6+37vQtfhkEud0+x9FQF10l5k5zE0lkxatj9F9lUPxtlAHq3ZTnbhA4oMsN6ep1B2Dvu6Sz19h2vR2vqSog5FgvteMq7Rxy88/zR1Rw6iNVoIezr50YCz+MUv5zJu0hQAtmzcQOnDRdQfa+XMdBV/GGckJ7WT67Y8iTbrorhqwKiqtEDwXUOtVvPQQw8pnjvrrLPijjteJjiKCIS/J0RvAlE1w/aWJjwJZPRDPjdlyx5BpdWROeU+edKr3l7GeRdcFFdiFNvf4qrdHZHob65Ha7TEqRe+9PzT2MbPp2FdRITLNmSmQiG686M3CPvdhFytqPQmQl43hrQMmjY8TDjgRa0z0rSxmLDfjcaSjkUdpL2zHd/OZYQGz6KtuYmheRbF9x6ap+HTLxzfwhUWCAQCgUDwfaOnSJblgtF4D9XAnlWKNU7SNrS0bHJuX50wceD5RNl/G80AR/p3R8jn0Kb1RQoF5ZY0TSjArXfdJ6/r/rSihCeW/A8lxfei0hmwWlO59Y4F8tpv0IiJ/DhHuT6Ksr2inOcW/4bnJsDQPKvcQlY82sjLBWoKX/1frps5gx1vdqtKP1S8gOk9+oIFgu8jIhD+HpFI+r+njL4uM5dwMEDmlYn936LKiNHssjXdjrOqFEP+aFy11dhjpPx7mqdHd0t1mTlIwYDihuGq3Y3z7RfJvupBhVG8NxBC0urIHHcHWqudhrVF2CfehXbvCm65MMymf6k54GjEuuMJ9AZDQmGss39k/9ausUAg+OHQEvzy7/mxzQ1A5z8+BYWHsEAg+CaIJgAauzKnN9x425fKDp+ISBYk7kV2VC0nfXikJzFaYhwbKCcTvQo013cHvhVLCXk7qXt8WneQO7c7CC5b9gghtUZePzn3rqHj/a2UFN/LqqeXMee2uxg0Ivn3fekPj/HnCSS0mfzgNjPt3hBLljx4wtdLIPg+IQLh7xGJpP9jZfSbq5ZjGz4bx9aSXtUIE5U8L39iEfYp9yUMnqPHRndL0wbPoGXnM4pSZ+fbL2GfMF9pp1RwD82vlSGFgzjfeoGcO59Da+tLYO+z3HZhmJc/DLC6wCSLYM0s91D4ipryq+Fwe5jf7/bxWatETqaWij1vMXno8G/6EgsEgh8ALUGwWE2cbTd+qfelp3SSHTqKd2MlQW1WUg9hgUBwcohNAOQm2aQ/HscTyYpy4KO/43O3415bhEpvhFAI639MkzO7aYNnxLV4qQ0piYVDjRbqSqahNqRguXgStiEzZZGvW2NKoF96/mnCOhP2ruSFq3Y37tpqsqcVKZISs0Lw49kzEn6/z+qOJKymO+AIJ/QIFgh+SAixrNOM6l3bmDO7gIIxg5gzu4DqXdvk1xqPfJ5057Fl50psw2djzh8hlzvH0pv/28gxEwl5XfK5XbW7Obr6ThrWPUBjwxfyGKKiDRpzOumjb0EKBmjc8BB1j0+TzeV7ji3Y1oB9wnzC3kh/TNqQmbQ7nWz6V5DVBSZGDdSi06gYNVDLmkITobDEpFckFu7w8afJJnwPWHl+YpDlLz1DxZ63vta1FQgEgmgQ/POsg5xj+ORL/ZcdOoru4Mf4VSIIFgi+DWITAFHxKWvXJv2JkkhwKlaYqnrXNq6ZfBmv7XwN+9Qi8u7eSHbhb1Ebzbje3yq/T2NORxMK0L5tCfXLZ9KwtgijwUhrRbzo1bxfP0Cf/gPJmlpE+vBZScfeeORzgm0N8vqpbd86dNkDadq0mLrHp9G0aTFS5gDWrFqW9PudmdefPXUhxXN76kIMtKm4pQrm3zM/yTsFgu8/IiN8GnG8nc9ku5pqYwoZY2/HkJOP91AN6oAHZ1UptvHdu5bH83+LnjvkasX51gtk9lIi/dLzT9PSVaLk9Xowjp1Ly86VSST+c7rM5T20LJ9KpzeI1QAHHGGG5mkUYxiap6HTL5GaZmBNgUpR5vPnSUHueHWNyAoLBIKvTEsQfpwrkas7iO3IvwkFDV/6HM3/bP8GRiYQCBJxomXNvZFIcDQqTBVdd3mDIbKnKQVI7ZMX4thUjGf7Mho7OpACXkzWNMJqHdlXdVssOatKI8e0OkjN7IOWEE8svg+0BjI6lBonPcee3f8Mmjs98vop4Kgn5OlQ2C81VSzF43KyfetmWRwrlht+9RtuXvwb/jyh22Zy5gYPAY2RZcuXMnHi2BO+VgLB9w0RCJ9GJCp9ji1PTuald+UVBeyPUR6ce9d9SVWjo/TsufnZzy6lensZ/jBkjp8XN4ZVTy+TS6p7nmfFimWYzhku+wLH+u/Zhs+mafOj9LGoWFOoZ2ieid/v9vHHd/0J+4HNOol2p5OheVbFtYmIZjVhOPy+4nlJb8Dg9x332qo1AZx+aAn4AdWX/m0EAsH3gzOzTZhdEAoaCOj7fIUzNAGgOfYZ2qAz6VEnOjcBhLDxWaMHSPkK4xEIvr+caFnz8UimOh1dd7nXFiWsagt53QRTUsm+6rcYcvI5uuqOOA0W2/j5sGcVQ4eN4fWdr2GfvBBbtMe4shSVWqMQzood+w033kbZskdwbCvBfMFYVHoTWT3Et7ImL6SxfBHPPFmWMBAeN7mQ/6vZT+Ha52n3SZxnVzPnEh1/+ZdY6wgEIhA+jTjezmdvu5qxVO/aRvXb1QlVo2N3QGMzz9Xbyxg5bCSVm9cmvBk0OI7FqUjHjumJJb9DCnhp3FiM5HOjtfUlbej1kVKiundYM8MkB72LRhv5Z2Nkx3LNVd09wjduCfD4vQtZ/uzz7KnrjAuSzzmjD2nTlb7BJlMKHo/7uNfW6Yf6QB/+Xa8iQ/y/QiAQfEXMF18AgM7fgEabCgN/nPC4E52bAmioddk55kxB7/Gc1LEKBKc7yRIAvVW4fRmi6y610Zww4FbpTYoERWwZcxRDTj51Rw7ymqOBrKlFyqzyhPk4tpaQcs6QhGOPrqGeWlZM5z9eR/J7Ep5f8ns4VvdZ0u/x7ls7Kb/GpFg3jRkYZO5Dj4iMsOAHjVjyn0acyM7niXjpJcose84ZzvInFvHE4vvQGM2kXDQhLuu7f88q+uQMTCz8YOurEM6KJdafOORqpfWtFwg6j9Gy/SlUgBSQ4sqg11+dgmFRB3OrvBxwhBloUxFWWyi8fBQAN/5hGc8XIAfJsyuC3PXwQo78//buPS6qct8f+IeZYZgFjHjB2xZI86RmHY/aPuY+amqIF0gzjTC3F1LbamzzQnihMrdbkDISwyCOFhszd4ip21sdxW118LQ7v/xl+7g9Ul4RLwmaMDMsmOv5A5kYmOEiA2sW83m/XvuVrDXzrO9ae17fWd951vM8ylCHdtRQw6g0Nnptz5aooNeJLIKJqMU01iJYVApc+6exLl/T1Nx0p9x47wc6FsFEdTW1A6ApnM0+XXPfFTBwDEoObkLXWmvtlh7cVK8wdTZzdM3kWOZKvfNeZf0dFL39TL11i2ufY809VMn+ZOcFuZ8/evTo6fLcfrx8DSNnOpkwa+e1Zl8novaEt/0y0pRfPpuyjEDdnmXD2S+rl0aq1UPsbMH3q9cuY0XiRmzevBFdp6x0eMQ5aORvceuI68kaah7vsSh9HWZULPt8M9SVJqePQQ/sqsCZl6oTt8ligyZJDwBQmMtRYtLgmQNKlJeVoUNQEESTL34stuC//2FyOK4gqCCKjtucM7EIJqIW01iLYOrTH+cMwbhZVP0DmzNNz018SoWcO3bsGD7//HOkpqZKHYqkmtIB0BhXc7CMGTUGXxxNh3b8EtisVvtTbUqNPyZMmIJTp75xKEydzRytO5oOs6iHb5dQlx0JAGAw6PFO8hrszMmqd+9Wc9/mrCAvObgJCgC/i1vi8vwe6t0LBUV36t1nPfxgrxZdt7bU0mWyiJzh16uMNPbLZ1OXEajbs1z2dS6C64z7dbbge7devTEmPArbMlNx+/N0mMt+gm+XEHR8Yg6UAZ0aHJMzJjwK27PSoBlXZ4zzxOUwHEzG83sr8OdpGnsPb+xfRKSE/7J0SUGRBf1DugIANuw4jKDJ1Us51azSqbnyd+zfmYWnxjsmRa1GBV1T7jWJiNzEGBCKm9d8oBZdP2XC3EQtsWHDBhQUFODhhx+WOpR2wdUcLKcKtmPx4mXYmZMFg5MC7Ivjhx06B5QBnWAzGVF6JA2W8hJ0D+ljf78Y+q8oOfCW046E20fSANgQFr8PVcVnkZ62Eduz0lB++yd069UbHbpUr/bROWIRAKBkfzKslQb4qDXQ+PpizvK1TscH11gUn4h5617Bh5N+mTBr3mfA+tQ1rX5t3cEdy2QROcNCWGYa+uWzscm0atTtWTbdbnzB99o9zy8ujkfm+2noErOhWWNyym//hCAnx7lVUYHnnpmEaXn/gTLRDK1GAV+FAj0CfWCy2KoT9mErVsfNBQBcvXatxbNEEhERydXQoUMxbtw45ObmSh1Ku+BqDpai4kt4J3kNuvXqjRWJG50+tly3c6DzuN9BGdAJKNiO7TsO2F9bU7g560jwUanho1ShovAkAMCi9IVm3BIE3bvHEg++hcp7q310enIB/Pv9m/2+a0x4FO6Yq4/h4+tb79w6+esxY8Z4+Kk2IC7lTRReuYX+D3TD66/Pw/OThkA0XG3ydTIG9G7ya92pqfe3RM3FQrgdaeoyAnV7llWaQJdjWq6mTqvX8zwmPAoajQbbs7Y0a0yOqzHOISGhSFkeh5Tlcfbte4+dQFx2DgqLS9A/pCtWx821jw8O7dXLLbNEEhERebK8vDzk5OQ4bEtOTkZkZCS++eabJrejUCig1Wobf6HElEqlJHF2D33Q5WPLv1qQWd0D+X4aNBoNIiZOdXjvkuWvYnNaimPnwLF0LF+22n4uk6fOgEajwda0jdCJIrrXem3pkc3oPP4lqLTBuP3ZFtgABEcudyj6Ok1eicpj78Lnvz7E1asX0T30QSxfvhoRE6fiVqUZ335xAK/u3Iprly8hrHcY1r4yFzHTxttj9LP64MVpw/HitH2w/Fhk3267UAy11dqka+TjI0LRR4AgPAxBqIRW03YlREP3t3L4XAPSfbapYSyE25HmLCNQu2e55pGTumOPl654zWVxGzFxKoaPCG9WfLPmLkRGneMYjm7But/PqffaaRFj7YVvXa/NicKKrHeBiJdbZZZIIiIiTxAdHY3o6OgWt2O1WqHT6dwQUevSarWSxPnb2S/Wuw8qPbIZnUbHwkepqr6viliC7Vlb6t37DB8RjuWAY+fAomUYPiLc4VyGjwjH8BHh+OL4YWzPSsNPpTeh8A+Cj68fbh9Jg2+XEPgPHIPyr3c7fUrv1p1b2JmX77Bdp9Ph+NHD+GhbGrQRSxA6bSAqi89i6WtpsBTfRHT4cABA3VEYNUvDaQO00JU37Xr7GkUojSJ6BNyB2EPAj5d+brP5Cxq6v5XD5xqQ7rPdmiw2G25XWtzebl+3t+gaC+F25H6XEXDnrIuA6wkNxoRHQW8BDnyyDVeLLiK0V0+s+/0cTK9V8O49dgJbavUEL31hbr2CODp8OBQ9OuONzR/gau6lFsdLRERE8uOuCZTq3gdB5YfO4xfb50kBnD9hV/f4zh6fdvXaMgA+vn4InrQUfiEDUXZyF6zf7YEPbCjbHgvfUfMRMLD6/sdVp8YdM7Dv43+HNsLxseHK/mOQsHk7Fm3MwkPdOuGlCRMx5ddDar2zer1zq78AsaJpM9KrzHcRpKoCHq0fR2tr7WWy6D7ZAKXF/YVwW2Ih3I60pKBt6ayLNQn+p2uXoPDzR+CQpxA64x2HCQ0AYO/OLJQWX0KQvxLFxdfwbnYOfFDdA7z32AmkvJeOD6MUGBkWiIIiPea9lw7c21/bc1PDMWJ6LP77H5ztmYiIyNu05gRKCqUCP5/40N5TG/SbmHqTgjbn+M5eqzj4FgIGjoHmgUEwnD0B4X8PYNdzfvcmszJi5l/SYbBaodR2bbDoKym+VGclkHttRde0VYV5h/bB2rErJo98wvE8O2hhaWKPMG5ehMXsB7XhKoB+TXuPm7i7w4aoBkuIdqbuI887c7LsEz201lTztRN8WK2ZEO3LL41fgm2ZqTBDCd/+o9Bdfx27pigwMkxwKHa3ZOfgtwOtWPKZEf9basXDwQr8dqAKW7JzXD4mTURE5I0ef/xxPP7441KHIRl3TqDkrFAtPbIZXSKXQaUNRumRzVBaTFiy7JdZlmsf33D2S5R9nQvTzzex5Z0NDq+5de0ylJoAdHl6jUOsXSevRMn+ZJT/LQ9aPwV2P6e2L280to8Ku54GpnyyGQHdHmyw6OvxgOP4ZvPXH2H3FIVDWx8+ZcbiPX+uVwjLSc39bXt8xJiko5A6AGod9nG/IxcgNH4vMHIBMjPT8MXxw24/Vu0vg5qxNF0mLUXZ19WzWfqFDIROp4N2/BL4/PgFdt1L0L5Kn+oEHaVt+9AtAAAWo0lEQVTAluwcnLt6C7v+x4T0SRpUvqpF+iQNdv2PCeeu3nJ7zERERCRft65ddj6W9j5WkHB2HxMcuRzl3+yx/1sQhHpr+/qFDITh7Je4+9UOdB63CGHx+9Dl6TXYuvVtpKdttN+DmSv1TmO1VhkQFr8X+qrqZY1qGxmmRIXJB9t3HGiwsP/dy8tgOPYuKq/8HTaLGWW3bzlt68cbpc2+LkTtHQvhdspZUtfe+6XU3Vx9GZluFwOoHttiM1VWj4G5XeI0QRcWl6CjoMQHUwSHIvmDKQI6Co6vJyIiIu9WM4FSbfe7gkRj9zF+IQNRfvsnp8cv+zoXXSYtdbjf6jhpOay+gn2bb5dQp7H6dgmFj1KFoC7dUFDkONayoMiC3iE9G419wuRnkPrGfPgVZOBq6jPooFE5beuhnsFNvh5E3oKFcDvlzl9KG+Pqy8i3cwgqr/wduqPpCArugariswjq0tVpgu4f0hVllRanRXJZK8xIR0RERPI1a+5C6I6m23tCa+43Zs1d2Oy2XN7HdAmx/7tugV1zfNPtq07vt8xlP8Fw9ktc/+AlmG5fxa19Sfj5q4/ssZZ+tgVBv4kBAKh+MxszD1hx4pIZJosNJy6ZEXvYhmlzXm5S/M9OHYfvc/6Ii1vexLoXX8K8Q45tzTtkxaJnn2/2dSFq7zhGuJ1qzlJKLeVsNr/Sg5tgMdwFCrbbJ3jIzEyDb/8xmHngAHZNMd+bxMGCeYetWB03F1uyc1BQpLePawFqiuRubo+ZiIiI5MudEyg5vY85shkdR822F9h1J6uqOc6WdzY4vd9SCB1w96sd6HJvVuiq4rMoOfgWyr/eDZUQiIDBkfZZqQMGjsXd0qt4Jm8vdJXVPcHPLXoZo8Ofava51IwDXrznz/jxRike6hmMpbOel/X4YKLWwkK4nWrLqeadfRktW7ba6ZfRjpws/HS3CtP2KFEumtE/pBtWx/2yRNK899LxYVT9IpmIiIiotpaueFG7HeCX+5gOXbojQOWDO0fSGiywa7Zlvp8GRPxyv3X3s82AxYguU1bWmyDr9l82wizqYfj7UQgP/Iv9PabC/8TiFUluOZ/JI59g4UvUBCyE26m2nmq+KV9GY8KjMGh0FB7q0wl9OosIOJOPSkWYfX9NMRxXax3h2kUyERERUWu436J6THgUNBoNtmdtsd9v/f73ryA1ebXzR6Yr9Qh7ZR/unvwzSvYnwVpVge69+nA5ICIJsBBux9z1S2lbmhYxloUvERERyUbExKkYPiLcYdvOnCynj0zXTJDV6YnZEB74F6BgO7bvONDWIRMROFkWEREREZFbOZvMq/YEWYDrSUzvmJ3/7/DRQ1gyZzyeDv9nLJkzHt/911F08tdDcelH6L//sQ3Pjqh9YI8wEREREZEb1R2iptQEOEyQBTifxPSOGQjUCugZrHHYfvTgXny2fT2yI4GRYYEoKLqLeVtfwyOWf2Bip+4wqzrC0uPBVj8vovaEhTARERERkZvVHqL2xfHDyMxMQ2WtCbLqTmJaUwQPDDOjo0Z0aOuT91KQHQn7yhpj+6jwYaQZcX/aj3EJq1gEE90HSQthnU6HhIQE6PV6mEwmrF69GkOGDJEyJLoPXxw/jJ05Wbh1b5KIWXMXym5sMlFtzE1EROROjU1iWrsI7nH+SyhhdXj/DxevYeTzWodtI8OUKCy+3TYnQNQOSVoIZ2dnY/jw4YiNjcXFixcRHx+Pffv2SRkSNVPNL5za8UsQeu8XzszMNABgMUyyxdxERETu5moS09pFcDfLdcDsi0p1d4fX9A/phoIivb1HGAAKiix4qFsnAIDy5kUAaNWeYaWqClYAN0orW+0YRG1J0kI4NjYWarUaAGCxWODn5ydlOHQfduZkQTt+icM6eRi/BDtzslgIk2wxNxERUVvqGaxBR40IX4MFFif7l74wF/PeS8eHUWaMDFOioMiCeYetWB23AAFDHgUAVP2/AqiK/3+Tj2lT+8HPWNWk1yqUJljQEecMwQCAzhxcSRKwWq1Yt24dCgsLoVarsWHDBjzwwAP2/YcOHUJOTg6USiX69euHdevWQaFwPTd0m32M8/LykJOT47AtOTkZgwYNQklJCRISEpCYmNhoOwqFAlqtttHX1aVUKu/rfW1NbnHeunYZoU7Wybt67bLT8xArzRAEAYIA+Kn94Ovf/HP1MZRDKfhDDTUEQQWtxvXHWA7XUw4xAvKJs7mkzk2AfK6tHOJsSYzV+UkDweoPiyBAE9D8dhQVfrAKAtR+VmhV8s5NAOMk7yCHIV41S0vGZeegsLgE/UO6YnXcXPt2jbUIvo/1hrXPQ01u00+thspobPLrzxqCcfOuP9Si2PiLiVpBfn4+jEYjcnNzcfr0aaSkpCAzMxMAUFlZibS0NBw8eBCCIGDFihU4ceIEwsPDXbbXZoVwdHQ0oqOj620vLCzEihUrsHLlSgwbNqzRdqxWK3Q6XbOPr9Vq7+t9bU1ucXbr1dvpOnndevV2eh6iGRBFDURRhMJYhUpz88/V1yhCKVbAqDRCFE3QmRqP05PJIUZAPnE2l9S5CZDPtZVDnC2JsTo/2aAQK2ARRZgszW9HY62CKIowVvlA18DNohyuJcA4qf2T0xCvaRFj7YVvDV/jT1CqqmDq0x+3lL/CzxWBTW5PUAgQq5pW1N4orYReJ6KzikUwVTNarCi64/7H5B9rYN+pU6cwatQoAMDgwYNx5swZ+z61Wo1PPvkEgiAAAMxmc6NP9En6YMP58+exdOlSpKWlYcCAAVKGQvdp1tyF1V8Y45e4nAWRSG6Ym4iIvIOch3jVLoLPGYJx864Ket3PTX6/IFRCbEbvLh+HptqUADoo3d/unTt3sGDBAvvfMTExiImpXn9br9cjMPCXH3uUSiXMZjNUKhUUCgWCg6sf3f/oo49QUVGBESNGNHgsST/SqampMBqNSEpKAgAEBgbau7dJHhqbBZFIjpibiIi8Q0NDvGShzz/BGBCKm9d8oBbFZhWrWo2qwafqiKTQuXNn7N271+m+wMBAGAwG+99WqxWqWsOQrFYrNm3ahEuXLiE9PR0+Pj4NHkvSQpg3lu2Dq1kQieSKuYmIyDs0NMSLiDzL0KFDceLECURGRuL06dPo16+fw/61a9dCrVYjIyOjwUmyavAhByIiIiLyShziRSQfEREROHnyJGbMmAGbzYbk5GQcPHgQFRUVePTRR7Fnzx78+te/xty5cwEAc+bMQUREhMv2WAgTERERkVfiEC8i+VAoFFi/fr3Dtr59+9r/fe7cuWa1x0KYWt2Xxw9h7453cbn4BsJ69UTsijVYOHeS1GERERERcYgXkZdiIUyt6svjh7D7/T/gT1E+GBkWiIKiu3gheRW6BZgwa4C/1OEREREREZEXanwUMVEL7N3xLv4U5YOxfVTwVVb/NzsSePetVKlDIyIiIiIiL8VCmFrV5eIbGBnmuMjYyDAlfrh0XaKIiIiIiIjI27EQplbVO6QnCoosDtsKiizo1+dXEkVERERERETejoUwtappc15G7GEbTlwyw2Sp/u8LR4CXV8ZLHRoREREREXkpTpZFrWp0+FMAgAW1Zo1+IXENnnl2EnAmX+LoiIiIiIjIG7EQplY3Ovwpe0F8xww81KcTAFHaoIiIiIiIyGvx0WgiIiIiIiLyKiyEiYiIiIiIyKuwECYiIiIiIiKvwkKYiIiIiIiIvAonyyIiIiIiIqImqzJacP5yudvbDX/M7U26xEKYiIiIiIiImsxX4YNfaX2lDqNF+Gg0EREREREReRUWwkRERERERORV+Gg0ERERkUzodDokJCRAr9fDZDJh9erVGDJkiNRhERHJDgthIiIiIpnIzs7G8OHDERsbi4sXLyI+Ph779u2TOiwiItlhIUxEREQkE7GxsVCr1QAAi8UCPz8/iSMiIpInFsJEREREHigvLw85OTkO25KTkzFo0CCUlJQgISEBiYmJjbajUCig1WpbK0y3USqVsoizrpbGLVaaIQgCBAEQrP6wCAI0AY2352Moh1Lwh1UQoPazQqtq3m29HK+3HGMG5Bt3e8dCmIiIiMgDRUdHIzo6ut72wsJCrFixAitXrsSwYcMabcdqtUKn07VGiG6l1WplEWddLY1bNAOiqIEoilCIFbCIIkyWxtvzNYpQihUQFSKMVT7QiWKzjivH6y3HmAH5xt3esRAmIiIikonz589j6dKlSEtLw4ABA6QOh4hItlgIExEREclEamoqjEYjkpKSAACBgYHIzMyUOCoiIvlhIUxEREQkEyx6iYjcQyF1AERERERERERtiYUwEREREREReRUWwkRERERERORVJB0jXFFRgfj4eJSVlUEQBGzatAmdO3eWMiQiIuYmIiIionZO0h7h3bt345FHHsGuXbsQFRWFjIwMKcMhIgLA3ERERETkaaxWK9auXYuYmBjMnj0bV65ccdj/17/+FdOnT0dMTAx2797daHuS9gjHxsbCYrEAAK5fv47g4GApwyEiAsDcRERERORp8vPzYTQakZubi9OnTyMlJcU+k77JZMLGjRuxZ88eCIKA559/HmPHjkXXrl1dttdmhXBeXh5ycnIctiUnJ2PQoEGYM2cOfvjhB2RnZ7dVOEREAJibiIiIiOTg1KlTGDVqFABg8ODBOHPmjH3fhQsXEBYWhqCgIADAY489hm+//RaTJk1y2V6bFcLR0dGIjo52um/Hjh24cOECFi5ciPz8/AbbUSgU0Gq1zT6+Uqm8r/e1tfYep1hphiAIEATAT+0HX//mt+FjKIdS8IcaagiCClqN64+xHK6nHGIE5BNnc0mdmwD5XFs5xNmSGKvzkwaC1R8WQYAmoPntKCr8YBUEqP2s0KrknZsAxklERM51DPLH5PH/7PZ2b9y4gbi4OPvfMTExiImJAQDo9XoEBgba9ymVSpjNZqhUKuj1eofvgYCAAOj1+gaPJemj0VlZWejevTumTp0Kf39/KJXKRt9jtVqh0+mafSytVntf72tr7T1O0QyIogaiKEJhrEKluflt+BpFKMUKGJVGiKIJOpP742xLcogRkE+c7tCWuQmQz7WVQ5wtibE6P9mgECtgEUWYLM1vR2OtgiiKMFb5QCeKrRJnW2KcRETUlnr27Im9e/c63RcYGAiDwWD/22q1QnXvR+e6+wwGQ6M/kEpaCE+fPh2rVq3Cp59+CovFguTkZCnDISICwNxERERE5GmGDh2KEydOIDIyEqdPn0a/fv3s+/r27YsrV67g7t278Pf3x7fffov58+c32J6khXBwcDA++OADKUMgIqqHuYmIiIjIs0RERODkyZOYMWMGbDYbkpOTcfDgQVRUVCAmJgarV6/G/PnzYbPZMH36dHTv3r3B9iQthImIiIiIiIgao1AosH79eodtffv2tf/7ySefxJNPPtn09twWGREREREREZEMsBAmIiIiIiIir8JCmIiIiIiIiLwKC2EiIiIiIiLyKj42m80mdRBEREREREREbYU9wkRERERERORVWAgTERERERGRV2EhTERERERERF6FhTARERERERF5FRbCRERERERE5FVYCBMREREREZFX8ZpCuKKiAosXL8bMmTMxf/583LlzR+qQnNLpdFi0aBFmzZqFmJgYfPfdd1KH1KBjx44hPj5e6jAcWK1WrF27FjExMZg9ezauXLkidUgN+v777zF79mypw3DJZDIhISEBM2fOxLPPPovjx49LHVK7I4f8xNzkHnLKT8xN1Nbklmfq8tS8U5ucclBdnp6T6mKO8nxeUwjv3r0bjzzyCHbt2oWoqChkZGRIHZJT2dnZGD58OHbu3ImNGzdi/fr1Uofk0oYNG5Camgqr1Sp1KA7y8/NhNBqRm5uL+Ph4pKSkSB2SS9u2bcNrr72GqqoqqUNx6cCBA+jYsSN27dqFbdu24Y9//KPUIbU7cshPzE3uIZf8xNxEUpBTnqnLk/NObXLJQXXJISfVxRzl+VRSB9BWYmNjYbFYAADXr19HcHCwxBE5FxsbC7VaDQCwWCzw8/OTOCLXhg4dinHjxiE3N1fqUBycOnUKo0aNAgAMHjwYZ86ckTgi18LCwpCeno6VK1dKHYpLEydOxIQJE+x/K5VKCaNpn+SQn5ib3EMu+Ym5iaQgpzxTlyfnndrkkoPqkkNOqos5yvO1y0I4Ly8POTk5DtuSk5MxaNAgzJkzBz/88AOys7Mliu4XDcVZUlKChIQEJCYmShTdL1zFGRkZiW+++UaiqFzT6/UIDAy0/61UKmE2m6FSed7HfcKECSguLpY6jAYFBAQAqL6uL7/8MpYtWyZxRPImh/zE3NR65JKfmJuotcklz9Qlx7xTm1xyUF1yyEl1MUd5Ps/+1N+n6OhoREdHO923Y8cOXLhwAQsXLkR+fn4bR+bIVZyFhYVYsWIFVq5ciWHDhkkQmaOGrqcnCgwMhMFgsP9ttVo9PsF7uhs3biAuLg4zZ87E5MmTpQ5H1uSQn5ibWg/zk3sxN8mXXPJMXXLMO7UxB7Ut5ijP5jVjhLOysrB//34AgL+/v8c+nnD+/HksXboUqampGD16tNThyNLQoUPx1VdfAQBOnz6Nfv36SRyRvJWWlmLevHlISEjAs88+K3U47ZIc8hNzk3swP7kPc1P7wzzT+piD2g5zlOfzmp+Apk+fjlWrVuHTTz+FxWJBcnKy1CE5lZqaCqPRiKSkJADVv9xlZmZKHJW8RERE4OTJk5gxYwZsNpvH/n8tF++//z7Ky8uRkZFhn8Rp27Zt0Gg0EkfWfsghPzE3uQfzk/swN7U/zDOtjzmo7TBHeT4fm81mkzoIIiIiIiIiorbiNY9GExEREREREQEshImIiIiIiMjLsBAmIiIiIiIir8JCmIiIiIiIiLwKC2EiIiIiIiLyKiyESRLnzp3Do48+io8//thhu9lsxrRp0/DKK684bM/IyKi3jYioNTA/EZEnYm4ici8WwiSJAQMGYMGCBUhNTcX169ft27du3YqSkhK8/vrr9m2HDh3C1q1bpQiTiLwQ8xMReSLmJiL3YiFMknnppZfQs2dPrF27FgDw/fffY9u2bUhKSkJQUBDMZjPeeOMNJCYmIjQ0VOJoicibMD8RkSdibiJyHxbCJBm1Wo2kpCScPHkS+/fvR2JiIqZPn44nnngCAFBRUYGLFy9i9+7dGDJkiMTREpE3YX4iIk/E3ETkPiqpAyDvNnjwYMyaNQuvvvoqevTogVWrVtn3dejQAR999JGE0RGRN2N+IiJPxNxE5B7sESbJjR07FmazGQMHDkRAQIDU4RAR2TE/EZEnYm4iajkWwiQpURSxbt06DBs2DEePHkV+fr7UIRERAWB+IiLPxNxE5B4shElSb7/9NiorK5GRkYGnn34a69atQ3l5udRhERExPxGRR2JuInIPFsIkmb/97W/4+OOP8Yc//AFarRZr1qyBxWJBSkqK1KERkZdjfiIiT8TcROQ+LIRJEnq9HomJiXjqqacwduxYAECnTp2wZs0afPrppzh58qTEERKRt2J+IiJPxNxE5F4shEkSb775JiorK/Hqq686bJ8yZQpGjx6N119/HQaDQaLoiMibMT8RkSdibiJyLx+bzWaTOggiIiIiIiKitsIeYSIiIiIiIvIqLISJiIiIiIjIq7AQJiIiIiIiIq/CQpiIiIiIiIi8CgthIiIiIiIi8ioshImIiIiIiMirsBAmIiIiIiIir8JCmIiIiIiIiLwKC2EiIiIiIiLyKv8HeIRd1glPpr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For better visualization, we report the decision boundaries alone (middle), with the training data (left), and with the test data (right). The plots show that the decision tree correctly identifies the region where the minority class samples lie. The decision tree however mostly classifies samples from the overlapping region into the majority class (yellow/blue color gradient).\n",
    "\n",
    "\n",
    "We will reuse the functions above for computing the performances and for plotting the decision boundaries. For the sake of code conciseness, we implement two functions for computing the cross-validation results (`kfold_cv_with_classifier`) and for plotting the decision boundaries (`plot_decision_boundary`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def kfold_cv_with_classifier(classifier,\n",
    "                             X,\n",
    "                             y,\n",
    "                             n_splits=5,\n",
    "                             strategy_name=\"Basline classifier\"):\n",
    "    \n",
    "    cv = sklearn.model_selection.StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=0)\n",
    "    \n",
    "    cv_results_ = sklearn.model_selection.cross_validate(classifier,X,y,cv=cv,\n",
    "                                                         scoring=['roc_auc',\n",
    "                                                                  'average_precision',\n",
    "                                                                  'balanced_accuracy'],\n",
    "                                                         return_estimator=True)\n",
    "    \n",
    "    results = round(pd.DataFrame(cv_results_),3)\n",
    "    results_mean = list(results.mean().values)\n",
    "    results_std = list(results.std().values)\n",
    "    results_df = pd.DataFrame([[str(round(results_mean[i],3))+'+/-'+\n",
    "                                str(round(results_std[i],3)) for i in range(len(results))]],\n",
    "                              columns=['Fit time (s)','Score time (s)',\n",
    "                                       'AUC ROC','Average Precision','Balanced accuracy'])\n",
    "    results_df.rename(index={0:strategy_name}, inplace=True)\n",
    "    \n",
    "    classifier_0 = cv_results_['estimator'][0]\n",
    "    \n",
    "    (train_index, test_index) = next(cv.split(X, y))\n",
    "    train_df = pd.DataFrame({'X1':X[train_index,0], 'X2':X[train_index,1], 'Y':y[train_index]})\n",
    "    test_df = pd.DataFrame({'X1':X[test_index,0], 'X2':X[test_index,1], 'Y':y[test_index]})\n",
    "    \n",
    "    return (results_df, classifier_0, train_df, test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "def plot_decision_boundary(classifier_0,\n",
    "                           train_df, \n",
    "                           test_df):\n",
    "    \n",
    "    fig_decision_boundary, ax = plt.subplots(1, 3, figsize=(5*3,5))\n",
    "\n",
    "    plot_decision_boundary_classifier(ax[0], classifier_0,\n",
    "                                  train_df,\n",
    "                                  title=\"Decision surface of the decision tree\\n With training data\",\n",
    "                                  plot_training_data=True)\n",
    "\n",
    "    plot_decision_boundary_classifier(ax[1], classifier_0,\n",
    "                                  train_df,\n",
    "                                  title=\"Decision surface of the decision tree\\n\",\n",
    "                                  plot_training_data=False)\n",
    "\n",
    "\n",
    "    plot_decision_boundary_classifier(ax[2], classifier_0,\n",
    "                                  test_df,\n",
    "                                  title=\"Decision surface of the decision tree\\n With test data\",\n",
    "                                  plot_training_data=True)\n",
    "\n",
    "    ax[-1].legend(loc='upper left', \n",
    "                  bbox_to_anchor=(1.05, 1),\n",
    "                  title=\"Class\")\n",
    "\n",
    "    sm = plt.cm.ScalarMappable(cmap=plt.cm.RdYlBu_r, norm=plt.Normalize(vmin=0, vmax=1))\n",
    "    cax = fig_decision_boundary.add_axes([0.93, 0.15, 0.02, 0.5])\n",
    "    fig_decision_boundary.colorbar(sm, cax=cax, alpha=0.3, boundaries=np.linspace(0, 1, 11))\n",
    "    \n",
    "    return fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us recompute the performances and decision boundaries with these two functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5,class_weight={0:1,1:1},random_state=0)\n",
    "\n",
    "\n",
    "(results_df_dt_baseline, classifier_0, train_df, test_df) = kfold_cv_with_classifier(classifier, \n",
    "                                                                                     X, y, \n",
    "                                                                                     n_splits=5,\n",
    "                                                                                     strategy_name=\"Decision tree - Baseline\")\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree - Baseline</th>\n",
       "      <td>0.007+/-0.003</td>\n",
       "      <td>0.006+/-0.002</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Fit time (s) Score time (s)        AUC ROC  \\\n",
       "Decision tree - Baseline  0.007+/-0.003  0.006+/-0.002  0.906+/-0.025   \n",
       "\n",
       "                         Average Precision Balanced accuracy  \n",
       "Decision tree - Baseline     0.528+/-0.072     0.786+/-0.046  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df_dt_baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HXbp0UdqvQ4cOcHFxwZIlS/Dw4UM8fPiQy7r40UcfVfj+YmNjsXbtWkRGRiI2NhY///wzOnfuDACwsrJCREQEIiIiEB0djfXr16ttVVXFyMgIUVFRePDgAeLi4uDr64tHjx5xgWTixIm4dOkSzpw5g/j4eGzevBlZWVmwtbXF5MmTceXKFRw+fBjx8fE4efIkAgIClIYtFdHX18eYMWPg7e2Nu3fv4unTp1i8eDFatWpV4jmro6+vj+joaGRnZ5e575QpU3Ds2DGcO3cOCQkJ2LlzJwIDA1X+4ujYsSN69uyJ5cuX48mTJwgLC8PJkye5z4cMGYL8/HysXLkSMTExuH37NtauXYumTZuW+YwUZWdnw9vbGxEREUhKSkJgYCD09fXRrl07pf0GDRoEuVyOr7/+GjExMQgPD4e/vz/GjBmjsiW3PM/txYsXSElJUfn5uHHj8PjxY2zZsgVxcXG4cuUKNm3ahFatWlX4WZKSKD5RfCqO4pPyc6P4pBkUmyg2FUexSfm5UWxqIGopKZdGuLi4MAsLC+6/Xr16sWnTprHffvtNab+MjAz25ZdfMjs7O2Zvb8/mzp3LUlNTuc///PNP9n//93+sS5cubMCAAezixYuMMaaU+ZAxxi5cuMA+/fRT1qVLF9avXz92/Phxxhgrkfnw5cuX7Msvv2S2trasW7dubMmSJezVq1cqz8mY6gyGRdLT09m8efOYg4MD69q1K5sxYwZLTk5mjDGWl5fHli5dymxsbJijoyM7ffo0c3NzU8p8WDwzpOI2sVjM5s2bx2xtbVmPHj3YnDlz2I4dO5ibmxu3f1BQEHN1dWVWVlZs9OjR7NGjR9xnISEhbODAgaxz585swIAB7Ny5c2p/VhKJhH3zzTese/fuzMbGhn3xxRfsxYsX3Ofjx49nW7duVXv89u3bmZWVFfPx8VHKFKju+KNHj7J+/fqxLl26sCFDhrDr16+rPXdmZiabPXs269q1K3N3d2c7duzgMh8yxtjff//Nxo8fz6ysrFjv3r3Zxo0blX5+6p6RYuZDmUzGNm3axBwdHVmXLl3YsGHD2L179xhjrMT9PH78mHl6erIuXbowJycntmvXLiaTyUqcs4i6DKCMMfbgwQPWu3dv1r17dyaXy5UyYxb55Zdf2PDhw1nnzp2Zs7Mz279/v9LnFXmW5B2KTxSf1B1P8akQxSfNoNhEsUnd8RSbClFsaji0GKNVoQkhhBBCCCGENB4Nfmg0IYQQQgghhBCiiCrChBBCCCGEEEIaFaoIE0IIIYQQQghpVKgiTAghhBBCCCGkUaGKcAPh7u5eYn2+77//HpaWljh8+LDS9m3btmHo0KEAAEtLS/zyyy8AgIyMDG4dveKflceVK1eQlpZWqfInJSXB0tIS8fHxZe577949WFpaqlzkvrpJpVJYWlri3r175dr/8ePH+P3332u4VIQQQgipbfX9XUuRWCxGUFBQufc/c+YM+vXrV659GWM4efKk0jrShNRFVBFuILp164aHDx8qbbt79y5atmyJu3fvKm3/888/0b17dwBAREQEunXrBgDYvHkzrl69WqnrJycnw8vLC2/evKnU8e+99x4iIiJgampa5r62traIiIiAQCCo1LVq0uzZs/H8+XNNF4MQQggh1ay+v2spOnToEM6cOVPl86jy22+/Yc2aNVQRJnUeVYQbCAcHBzx69Ij7O2MMv/76K6ZNm4bff/8dMpkMACCXy/Ho0SMuOJuYmHALxVdlJa2qrsLF5/NhYmICPp9f5r46OjowMTGp0vUIIYQQQiqivr9r1dS5avPchFQnqgg3EA4ODsjKykJcXBwA4OnTp8jPz8eYMWMglUrx999/AwCioqIgkUjg4OAA4N2QHH9/f5w7dw7BwcFKQ1/++OMPDBkyBFZWVhg3bhwSExNVXt/V1RUAMGDAAAQFBcHf3x8zZ87EhAkT4ODggJs3byI1NRXz5s2Dg4MDunTpgqFDh+K3334DUHJotKWlJc6fP4/BgwfD1tYWEyZMQEJCAgDlodFFx/3000/o378/7O3tMXPmTGRmZnJli4iIwODBg2FtbY3p06dj3bp1WLp0qdpnuXPnTvTq1Qs9e/bEuXPnlD4r7R4mTJiA5ORkrFy5kjv/tWvXMGzYMFhZWcHe3h7z589HTk5OOX6ihBBCCKlL6tq7FgCEhYXBw8MDXbt2xbBhw3Dz5k1u/6dPn8LT0xM2Njbo3bs3fH19IZVKERQUhJ07d+KPP/6ApaWlymulpKRg+vTpsLGxwfDhw5GUlKT0ubr3m6SkJEycOBEA0LlzZ9y7dw8FBQX49ttv4eTkhM6dO8PFxQXff/99RR8/IdWOKsINROvWrWFqaooHDx4AKByq061bNwiFQtja2nJDdv78809YWlqiadOmSsdPnToVAwcOxCeffIKzZ89y20+fPo1ly5bh7NmzyM7OxqZNm1Rev2h4zalTp+Du7g6gMEh+8sknOHbsGOzs7LB48WJIpVL88MMPOH/+PFq3bo3Vq1ervaedO3di+fLlOHr0KNLT07F161a1++7duxebN29GQEAAHj58iAMHDgAAEhMTMWvWLHzyySc4f/48rKyscOLECbXnOXXqFI4ePQofHx8cOnQIgYGBSp+Xdg/+/v5o3bo1li5dihUrViAxMRFz587FmDFjcPnyZfj5+eHu3bs4efKk2usTQgghpG6qa+9aT548waJFi/D5558jODgYo0aNwpw5c/D48WMAwKJFi9ChQwcEBwdj+/btuHDhAs6ePQt3d3dMnToV1tbWiIiIUHktLy8vyOVynDlzBtOnT8fRo0e5z0p7v3nvvffg7+8PALh58yZsbW2xb98+XL16FTt27MCVK1cwbNgwrF+/HikpKZX5MRBSbagi3IAoDtm5d+8eevToAQDo0aMHF5wfPHjADdVRJBKJIBQKoaOjg+bNm3PbZ8yYgV69esHS0hIjR47EkydPVF676JhmzZpBKBQCAIyMjDB+/Hh8+OGHMDAwgIuLC1atWoWOHTviP//5Dzw9PRETE6N2CM2kSZPQq1cvWFlZYezYsUrDkYqbM2cOunbtCgcHBwwePJjb98yZM+jcuTPmzJmDDh06wMvLCzY2NmrPc/r0aUyYMAEuLi746KOPsHbtWqXPS7sHIyMj8Pl8GBgYwNDQEDKZDCtWrMDo0aNhamoKR0dHfPzxx3j27Jna6xNCCCGk7qpL71oHDhzAiBEjMHToUJibm2Ps2LHw8PDAsWPHABTOKW7WrBnef/99ODg4YN++fXB0dIRQKIS+vj4EAoHKqWbR0dG4f/8+1q5diw8++ADu7u4YM2YM93lp7zd8Pp9rAGjRogV0dHRgYWEBb29v2NjYwMzMDDNnzoRMJqOcKkTj6l62IVJp9vb2OHPmDORyOX7//XfMnj0bANC9e3fs3bsXMpkM9+/fx1dffVXuc5qbm3N/NjQ0RF5eXrmPbdOmjdLfx44di0uXLuGPP/7A8+fP8ddffwEAN6emtGsbGBiUmiVa3b5Pnz5Fly5dlPbt2rUrsrKyVJ4nJiYGM2fO5P5uYWEBXV3dct1D8eRd7dq1g46ODvbs2YPo6GhER0fj2bNn8PDwUHsfhBBCCKm76tK7VkxMDKKiopRGrxUUFMDa2hoA8OWXX2L9+vU4deoUnJyc4OHhUeKdSJVnz57BwMBAKYFply5d8NNPPwGo+PuNm5sbbt++DV9fX8TGxiIyMhIAKJkW0TjqEW5AHBwc8PjxYy6jYadOnQAAVlZWAAqz+CUkJHBzVsqjePKqiiRAUKxAyuVyTJ06FQcOHMB7772HadOmYePGjaUer62tXe5rq9tXVfKtsu6h+OdF56joPTx58gQeHh6Ijo6Gvb09vL29uWHjhBBCCKl/6tK7lkwmw7Rp03D+/Hnuv5CQEGzZsgUA4OnpifDwcMyZMweZmZn44osvuGHLZSleBsXG/oq+32zbtg0LFy4En8/HZ599hlOnTpWrDITUNOoRbkDatWuHpk2b4uzZs+jWrRt4vMJ2Dm1tbdjZ2eHMmTOwsLAoMWeliJaWVqWvXdaxz549w2+//YZbt26hZcuWAMDN1a3J7IIffPBBiTWA//77b5iZmand/9GjRxgwYAAAID4+nlumoKL3cOHCBdjZ2SnNbY6Pj0fbtm2rfmOEEEIIqXV16V2rffv2SExMVHqv2LFjB4yMjDB69Ghs2rQJ06ZNw4QJEzBhwgTs3r0bwcHBmDt3bqnlsLCwgFgsRmxsLDp06AAAXC8uUPb7TfFz//DDD1i1ahUGDRoEANwUMcouTTSNeoQbmG7duiEkJISbs1Kke/fuCA8PVzlnpYi+vj5evHhRqeQF+vr6AApbCcVicYnPmzRpAh6Ph0uXLiE5ORlXrlzhWiXz8/MrfL3yGjVqFP766y8EBATg+fPn2Lt3L37//Xe1vwA8PT1x/PhxXL58GVFRUVi5ciX3S6489yASiRAbG4tXr17ByMgIUVFRePDgAeLi4uDr64tHjx6hoKCgxu6XEEIIITWrrrxrTZ48GVeuXMHhw4cRHx+PkydPIiAgAObm5tDV1cUff/yBdevWISYmBk+fPsXNmzfRuXNn7lxpaWkqM1R37NgRPXv2xPLly/HkyROEhYUpJfos6/2mqJyRkZHIy8uDkZERrl27hsTERPzvf//D4sWLAdTs+x8h5UEV4QbGwcEBb968KRGce/ToAYlEUmpw/uyzz5CQkIAhQ4ZUuJWuWbNmGD58OBYuXKiUCbFI69atsWbNGhw6dAgeHh7Yu3cvVq5cCW1tbS67YU1o06YNduzYgXPnzmHw4MH4448/4ObmVmIodZGhQ4fCy8sL3t7e8PT0hLOzM0QiUbnvwdPTk2v5nDBhAuzs7DBlyhSMGTMGycnJStkcCSGEEFL/1JV3LRsbG2zevBmnT5+Gh4cHDh8+DB8fHzg7OwMoHJKcl5eHUaNGYdy4cTA1NcWqVasAFC7BxOPxMGjQIGRkZJS41vbt22FsbIwxY8Zg27ZtmDBhAvdZWe83FhYWcHR0xLhx43Dz5k34+PggKioKHh4eWLJkCT799FPY2Ngo9TIToglajMYlkAYsKioKUqmUm8MDAP/9739hZWWFuXPnarBkhBBCCCGEEE2hHmHSoCUkJGDy5Mm4ffs2kpOTcebMGdy5cwf9+/fXdNEIIYQQQgghGkI9wqTB27NnD06dOoWMjAy0b98e8+bNg5ubm6aLRQghhBBCCNEQqggTQgghhBBCCGlUaGg0IYQQQgghhJBGpU5UhDMyMtC3b1/ExMRouiiEEKKE4hMhhBBCSMMj0HQBCgoK8PXXX0MoFJZr/7x8KSS5FV+HlcfjQS6XV/i42kblrF61WU4pAKEOH7oCObQAaMnzwVhhWxPLl6kvI58HuawePMsaLKe2YdMaOW9VVSQ+VTY2AfR9qk71oYxA7ZdTMT7xFGIT8nLBtFQvJwdQfKqrsamiqhKfalN9+f4WR+WuGKV4JMmGHDrlPra+xKTiaqLcDSU+aZLGK8LffvstxowZg++++65c+0tyCxDxv9gKX8fQ0BDZ2dkVPq62UTmrV22WM1MKfNC+Gdo3l0AHMmiLE1FQULiovOzFK/VlbGKI7Nf14FnWYDlNHAfWyHmrqiLxqbKxCaDvU3WqD2UEar+civFJJI7jYhMv7ikKdFqpPa6xx6e6GpsqqirxqTbVl+9vcVTuilGKR3+FIZdnXu5j60tMKq4myt1Q4pMmabQiHBQUhObNm6NPnz7lrgjzeDwYGhpW+Fp8Pr9Sx9U2Kmf1qs1ySnKl0NPTg54eoC/gQUuuD6Gw8NqyJup7hPk8Pgyb1INnWU/KWV0qGp8qG5sA+j5Vp/pQRqD2y6kYn/QUYpNcTw9Ckfpy1JfvfX0pJyGEkLpDoxXhwMBAaGlp4c6dO3j8+DGWLFmCPXv2wMTERO0xcrm8Uq1X9aW1jspZvWqznBIpIJEIIZFIIIMM2pI3KHg7Ek1WSitgfWndrMlylm9iRO2qaHyqbGwC6PtUnepDGYHaL6difOIpxCaeRIICGcUndepibCKEEFI9NFoRPnHiBPfnCRMmYM2aNaVWggkhpLZQfCKEEEIIabg0PkeYEEIIIYQQQkjtKpQwSGQAACAASURBVCgoQEJCAnJzczVdFLWEQiHMzc2hra0+sWNl1ZmK8LFjxzRdBEIIUYniEyGEEEIamoSEBPD5fLRs2RJaWlqaLk4JjDGIxWIkJCSgY8eO1X7+OrGOMCGEEEIIIYSQ2pObmwuRSFQnK8EAoKWlBZFIVGM91lQRJoQQQgghhJBGqK5WgovUZPnqzNBoQgghhBBCCCF1S2xsLHbv3o3c3FxIJBL06tULtra2uHDhAtauXavp4lUaVYQJIYQQQgghhJSQnZ2N1atXw8fHB2ZmZpDJZFi5ciVatGih6aJVGVWECSGEEEIIIYSUcOvWLdjb28PMzAwAwOfzsWrVKjx69Aj3798HAJw9exY3btyAVCqFgYEBfHx88M8//8Db2xsCgYA7RiAQ4OuvvwZjDFKpFIsWLaqRJFjlRRVhQgghhBBCCCElpKen4/3331fapq+vzy1nJJfL8fr1a/j5+YHH42HBggV4/PgxoqOjYWlpiXnz5uHBgwfIzs7Gv//+CwMDA6xZswbPnz+HWCzWxC1xqCJMCCGEEEIIIaSE1q1bIyoqSmnbixcv8OeffwIAeDweBAIBVq9eDX19faSmpkIqlWLQoEE4ceIEvvzyS4hEIsycORM9e/ZEYmIilixZAoFAgMmTJ2vgjt6hrNGEEEIIIYQQQkro3bs37t69i6SkJACAVCqFv78/jIyMAADPnj3DrVu3sG7dOixYsACMMTDGcOvWLXTt2hU7duxAv379cPz4cdy/fx8tWrTA9u3bMXnyZAQEBGjy1qhHmBBCCCGEEEJISSKRCCtXrsS3334LxhjevHmD3r17o23btvjzzz9hamoKoVCIqVOnQkdHBy1atEB6ejo6d+6MtWvXgs/ng8fjYd68eWjdujW+/vprnD59Gnw+X+M9wlQRJoQQQgghhBCi0ocffgh/f/8S2+3t7QFA5WcA8N1335XY5ufnV72FqwIaGk0IIYQQQgghpFGhijAhhBBCCCGEkEaFKsKEEEIIIYQQQhoVqggTQgghhBBCCGlUqCJMCCGEEEIIIaRRoYowIYQQQgghhJBGhZZPIoQQQgghhBBSq+RyOTZv3oxnz55BR0cHS5cuhampaa1dn3qECSGEEEIIIYSU6mxgIOx6fIyWrVrBrsfHOBsYWKXz3bx5E/n5+fjuu+8wc+ZMtesR1xTqESaEEEIIIYQQotbZwEAsWvkNRG5zYTa8E/KSIrFo5TcAgJEjRlTqnA8fPkTPnj0BAF26dMGTJ0+qrbzlQT3ChBBCCCGEEELU8tm4BSK3uRC2tYYWXwBhW2uI3ObCZ+OWSp9TLBZDJBJxf+fz+ZBKpdVR3HKhijAhhBBCCCGEELWS4mKga9pJaZuuaSckxcVU+pwikQhv3rzh/i6XyyEQ1N6AZaoIE0IIIYQQQghRy7RdR+QlRSpty0uKhGm7jpU+p5WVFe7cuQMA+Ouvv9CxY+XPVRlUESaEEEIIqSdkMhmWLVuGMWPGwNPTEwkJCZouEiGkEVi+eCHEYf7IjX8IJpMiN/4hxGH+WL54YaXP2bdvX+jo6GDGjBnYsWMH5s2bV40lLhslyyKEEEIIqSeuXbsGAPjhhx9w7949bNiwAXv27NFwqQghDV1RQiyfjVuQeDoGpu06Ys361ZVOlAUAPB4Pixcvrq4iVhhVhAkhhBBC6gk3Nzc4OzsDAF68eAFjY2PNFogQ0miMHDGiShXfuoYqwoQQQggh9YhAIMCSJUsQGhqKHTt2lLk/j8eDoaFhLZSsavh8fr0oZ3FU7oqR5Eqhp6cHPT1AV0cX2vrlLwOfx4dhk3r4rOtpuRs6qggTQgghhNQz3377Lb766iuMGjUKISEh0NfXV7uvXC5HdnZ2LZaucgwNDetFOYujcleMRApIJEJIJBLw8vOQKy1/GQybGCL7dT181jVQbmG1nq1xomRZhDRgQaHX0HfcZLR28kDfcZMRFHpN00UihBBSBefPn8fevXsBAHp6etDS0gKfz9dwqQghpP6hHmFCGqig0Gvw3eWPgx48OJobICIhB1N3+QMAhvd30XDpCCGEVMaAAQOwbNkyeHp6QiqVYvny5dDV1dV0sQghpN7ReEVYJpNh5cqVeP78Ofh8PjZs2ABzc3NNF4uQes/v0BEc9ODBpX3h19ylvQAHPaSYfegIVYTLgWITIaQu0tfXh5+fn6aLQQgh9Z7Gh0YrLgMwb948bNiwQcMlIqRheJqUBkdz5eFyjuZ8PE1K01CJ6heKTYQQQgghNe/vv//GnDlzav26Gq8Iu7m5Yd26dQBoGQBCqpOlqQkiEmRK2yISZLA0NdFQieoXik2EEEIIIe8EBZ6FUw87tGrVEk497BAUeLbK5zxx4gR8fX2Rl5dXDSWsGI0PjQYqtgxAZZcAqC+p7amc1as2y6m4HIC+gActuT6EwsJry5rI1B5XUyn1l3wxA9O2bMYBdykczfmISJBh2iWG1QtnVOp6jTH1f23EJoC+T9WpPpQRqP1yKsYnPYXYJNfTg1Ckvhz15XtfX8pJCCH1VVDgWWxYtRAHBwKO4wwQkZCBqasWAgCGjxhZ6fO+//778PHxwdq1a6urqOVWJyrCQPmXAajsEgD1JbV9Qyvn9fAQHD+yF6nJcWjZph3GT5oBZ1ePWihhodp8norLAcggg7bkDQoKCj+TlZIyv6aWAnB37IVcyRzMPnQET5PSYGlqgiVfTIK7Y69KXa8mlyyoy0sA1HRsAhre916T6kMZgdovp9JyJQqxiSeRoEBW+/GputVUOetybCKEkNq0faMPDg6Ecu6ZgVLM3uhTpYqwi4sL/vnnn+oqZoVovCJ8/vx5pKSkYMaMGbQMQANzPTwEe/Zsh+GAuTAz7YS8pEjs2bMdAGq1MtyYDe/vQomxKoliEyGEEFKSpjs5iGY8jUuC4zgDpW2O5nw8jUvSUImqTuNzhAcMGIDIyEh4enpi2rRptAxAA3L8yF4YDpgLYVtraPEFELa1huGAuTh+ZK+mi0ZImSg2EUIIqauuh4dg+sQhGOJqjekTh+B6eEitXXfPnu2A43SYLQwCHKdjz57ttXZ9ojmW7UxV555pZ6qhElWdxnuEaRmAhis1OQ5mpp2UtumadkJicpxmCkRIBVBsIoQQUhdpcsSdYicHgML/v+3koF7hhm3+4uWYumohDg58l3tm6mVg2brlmi5apWm8IkwarpZt2iEvKZILlgCQlxSJlm3aaa5QhBBCCCH1mCYro9TJ0XgVzQOevdEHT+OSYNnOFMvWLa/S/OAi7733Hvbt21fl81SUxodGk4Zr/KQZyP7ZH7nxD8FkUuTGP0T2z/4YP2mGpotGCCGEEFIvpSbHQVdFZTS1lMpodQ2lLurkUNRQOjmCQq+h77jJaO3kgb7jJiMo9Jqmi1TnDB8xEjfv/YGUlFTcvPdHtVSCNYl6hEmNKWqVPH5kLxLfJlSYNWt+nRs6Q0kfCCGEEFJfVHTEnbqh1EKhED17u1bo2uMnzSgchj1gLnTfniv7Z3/MmjW/KrekcUGh1+C7yx8HPXhwNDdAREIOpu7yBwBKOtqAUUWY1ChnV486XamkzNaEEEIIqU8qWhlVN5T6wD7/CleE60snR0X5HTqCgx485aWBPKSYfegIVYQbMKoIk1pTF3teKekDIYQQQuoTVZVR5z7OOH5kL7b6LCvxjqV2Xm9ibKWv39DekZ4mpcHRXMXSQElpGipR7WGMQUtLS9PFUIsxVmPnpoowqRXl7XmtamW5+PHTZ3iV2trZUJI+BIVeg9+hI3ialAZLUxN4TZlELZiEEEJIA6VYGS3rHUvdUOpWZh3KvM718BDs37sdWen/QktbCENDQ3w+a2GDqwhbmpogIiGH6xEG3i4NZGqiwVLVPKFQCLFYDJFIVCcrw4wxiMViCIXCGjk/VYRJrShPz2tVhymrOn7bdl/Mys1Ve3xDyGxN81oIIYSQxqusdyx1Q6kXLFha6nmvh4dg587NMBq4AE3fHpd+aRv8t28A0LCmkHlNmYSpu/xx0ENhaaAQOZbOnqTpotUoc3NzJCQkIDU1VdNFUUsoFMLc3LxGzk0VYVIrytPzWtVhyiqP71/68Q0h6QPNayGEEEIar7LesdTN6+3/6VBkZ2erPe/xI3thNHCB0nuVsfsCZFzxb3BTyIrel2YrjK5bOrvhj67T1tZGx44dNV0MjaGKMOHU5Bze8vS8VnWYcmWObwhJHxrzvBZCCCGksSvPO1Zl5vWqe6+SZqUgNatKRa6Thvd3afAVX6KM1hEmAN4NK4bjdJgtDAIcp2PPnu2VXmeuuLLWFL4eHgK+UFSltekU17YTR97AiwNfIGHLMEBbF54j+1XbvdQ1hfNaZErbGsO8FkIIIYSU/Y5VWerWDBY0bVWvppARog71CBMANZ89ubSe16JKuL6NO9Iv+8F4oFelhikXDXOWWDpBHHld6Tzq5rQ0hOWTGuu8FkIIIYTU3Oi28ZNmcHOEFd+n+LICjJ9Uf6aQEaIOVYQJgNrJnqxuWI5iJVzH2ByZYQEoyEiEQGgAry9XljuQF+3nt3U9jD9bVuacluvhIfDbuh4tiu1b35ZPaqzzWgghhBBSqCaWNCo63/6925GimDV67rJ6845ESGmoIkwA1Hz25NLmHytWwkWd+kLUqS+YTIrELcMrHGidXT2w1WcZdMuY01LUEyzNzVG5b31bPonmtRBCCCGkujWUNYNpmUmiCs0RJgBqbn4JUPb8Y3VzUCpbCS/PnJbjR/ZCYOkELR39ar02IYQQQkhlXQ8PwfSJQzDE1RrTJw5psPlNalPRMpO7XHKQu8IAu1xy4LvLH0Gh1zRdNKJh1CNMANRs9uTKrG/36vI26PK1MMTVutwZrIt6nVOSnkMraD1YgQTazc2gZ9EL4r+vKs1pSU2Og0AihaHdIGRc9kMLhfnEacEb8anbwCrfNyGEEEJIeTWEvCV1ES0zSdShijDhVPfwF8WKqXkF1rdr0qIVmLQAegMXw6icvwiKfnkILJ3AzxbDeKAXpNnpyLp9Eq/vnIaWthB8gQBbfZZh354t4OnqoSAjEYgC9Do6vJ2XnATt5qaQi7Nw/dZ1fNTFhn7xEEIIIaRW1HTi0saKlpkk6lBFmNQIxVZN7dCACq1vN33iEAjdlH8RSCyd4Ld1Pbb6LCusKMtlyH6ZzvUWF/3yyAwLgPFAL8jEL5EVcUKppzf90jaILPtAHPsbjIeu4LZnXPaDkdNEiDr1RW78Q2SGBcDQbSb94iGEEEJIralq4tLS8rE0ZoXLTOZwPcIALTNJCtEcYVIjFFs1m348GhmX/UpdQ1hxPkxK0nOlBFbiyBsQR15Hi8+WwWxhEIRucyGWMjR3n8/NN05JLjymICMJuqadkHXnFFoM9IKwrTW0+AIuc/SbJzdh7L5AaXuLgV7I+uUUcuMfIuOyH5r2Gg1d005IrWcJswghhBBS+0KvnK+Web1VyZlSVj6W+igw9Brsx81CK6dBsB83C4GVnNPrNWUSpobIce25FAUyhmvPpZgaIofXFFpmsrGjHmFSLsVbGafP8ELP3q5q94FAF82z0wEUZoIGgMzQwmWRWpm2L7GGsOJ8GH7wJry6fRLNnCYAALLunILx20ot8G45pMywALw/bTcwYC7yL2xAXlIktFuYIi8pkqsQK9I17QR5rljl9oKMRGSGBSj1DCv+4qFWVkIIIYQUdz08BHsCtsOwf9Xn9VYlZ0pDG1YdGHoNi3YehWiAF/dcF+30g1BPDx6OPSt0rtKWmaRs0o0bVYRJmVRVVrdt98Ws3FylNXmL75N6wRevIk5A9joN2i1MoWfRC/rRBdh/9CJ3blWB23jwIqSd94Ze265cJVV15TUJ4sgbyPrlFKSSHKQHb4LIegDSL/tB0LSVyuHYWrr6KrfzhPpo7jYTuqaduB7rWbPmq703Sl5BCCGEkONH9sKwf/VUQKuSM6Wqw6rrGp9DpyEa4FXsuXphTcB3Fa4IA6qXmSzKJn3QgwdHcwNEJORg6i5/bn/S8NHQaFJC8aHK+/du5yqrRcOJDfsXBvkiihVaLb4AMvFL8LR1YTzQC+YLg9DcbSZyHv6MnOzXSsN0UpPjVPfc5r0BIvYjcctwCIQGKocK8ZuY4NXNo2jefybMvzoHkfUA5Nz/EbKsVMjEmUi7uFFpOHb6pW0w6OxSYph22sWN0NXWxutgXyT6jUXKDyugrfDNKH5vwrbWMBygfP81ISj0GvqOm4zWTh7oO24ypfknhBBC6hh17zHlmV6laqkkZ1cP7D96ERfDH0Io1EOzwYvL9f5R3UtRalpSkupOkISEhGq7hmI2aW2+1tts0jz4HTpSbdcgdRv1CBMlqno/s35YgaZltDIWb4nMunOKm4sLFLbkmQxehNRz3ti3ZwvXklkUuIv30LZq057rOebmvQyYy2WClr76F1q6+jC0G8Qd28xpAvTadgUi9mP/0Yu4Hh6CE8f2ITExFhDooPmAL2DQpR/EbT7iskRrvd0uMDRGevAmGHYbCKPeY5VaXTXRykqtlIQQQkjdp+49pqwKaHlGm1Xk/UPVsGrF0W31jampmcrnam5uXm3XoGzShHqEiRJVvZ8Co9ZltjIWb4lUN0eX5b1BtjgHg12tMMyjF1KSniM9eJPKRFpFLaVbfZZBmwe8DvbFy2sH0eLTuTD/6hxaDlsBceT1t8m0buDFgS+QcmolUlP+4VpVfwgMx8Xwh2jZ6n0IDI0BFM5Zfn/abrQavR6Cpi1h0KUfNyRbEn2nRKurJlpZqZWSEEIIqfvGT5qB7FB/tQlB1SnPaLOKvH84u3oUVnrfjqZDxH4uH0tVqOq1rg3Lp4yC+GflEXzin/2wZubEartGYTZpmdI2yibduFCPcCNXPAlU0Zq/4sgbyLpzCgUZSeCLmiPt4kaYDFn8rpUx1B+zZr5rZSzeEqlqju6r2yfBExnBZPCid0saXfaDTsv2SDvvDXneG7Rq055rvSzeUpoTvAkGXQcozyce6IXUc95geW8gMGqNFu7zITA05lpVBw8do7J8Rddu5vQuoOqadkJBeiJeHPiCW1O4ICMRC1f41norK7VSEkIIIXWfs6sHhEIh9u/1Q+LbdynnPs44fmQvtvosU5vgqjy9vRXt5VVcirI6aDJHyoi3o998Du1FYlIiTE3NsGbORIxx74/s19nVcg2vKZMwdZc/DnpI4WjOR0SCDFND5Fg6m7JJNxZUEW7E1GVsTrvgi4LU50pr8Kae80ZumD9SM1LQsk07LJi/VClrdPEED4bNjJF5aQuauy98F7z/+BEth60oUZHNDAuAydAV3JBmoHAtYVVJtDJDA7hs0sDbXub8NzD/6pzSmsBFrapFFeGi8m3d9A1YgQQ8XREMbD24jNbAu4p6UdKsvKRIpAdvAgDMmjWfu7eWbdpVSytradSteaevzWD72WgI+FpITM+mDIeEEEKIhvX/dCj3TlTeymN5hlQXf7dSfP8o6shISX4OgdAAUkkOWpm253qiq2OlC01noh7R34WrEKtTlazPpWWTJo0DVYQbMXUZm1OD1qPl8JVK21sOK6yoHg9/CAAwNDREdrZyi5xiwE59WxlOO7ce8nwJtFuYgeW/UZv9uagV9Hp4CHZv90Zufj7YqZXQbmGKpr1GQ9Spb+G+mYlKxxcumWSmtCZw+mU/tPl8L9eqqryskw5auC+AFo+PVzePcpmpCyvqwWg5bGWJ5+G3dT1kuWK0bNMOXy7fwN1jaS29VaWqlXLyBQkmWGsjJDoHhwbp0dxhQgghpI4pb+VRXW+vcx9nTJ84RKkSq7jaBqBc2TZXGOUmMXPA9s1rwQDI899Au7kZJGYOle7FVddrnZD0HJ4j+0GLx8frjBQYNjPm/lybS0xWRz4VVdmkSeNBFeFGTF2AY/kSlRXWspJDqWoFzbu8DUygg2ZuM5F+abvK1k9+E5PC7QZNsMV3JXhCA7Qc8TX3iyHjsh8AgC9qBi2BDpL3fg5pVgoETVtBlvcGLdz+q1ROWVYq0i74gi8Uwbl7O/B09WFgOwhmY7YW/rK4tA3N+k6GkdPEt0mzEsHTFYHlqb5vaW4OzBcW9jjv3LkZTFqAZoMXl2jpFcsY1v2wE7EJybBs/z6+8pqIoR6DK/xzAZRbKZ8kpqJDMy34ugrhfSsPhz/T43qKC+cOSzH70BEK5IQQQoiGlTfBlareXuc+zrh+63qZvckqOzIGeiE9ZCuYQEdpClrGZT/od3Iudy+uYucBXyhS+94mljIYu8+FIDsdr24dg7H7XDSt5eHTivlUAHonIhVHFeFGTN2wHIFe4XJFMvFLbp6woGkrGDYzLvV8qgKz0cAFyA3zByL2QyZ+hfRL22DsvuDd0ONL2yATv0bK6dXgCUXQehvAFc/RYqAXMq74g1cggUDPAM0/VZjnG7JFqQyFawUbIO/FE5h8tozbL+3CtxBHXofsdRoETVshM3wfTL84DL6oGdIvbYNRnwl4deOIyueh2ONsNHABMq74l2jp3b9nM5po5eCIh9bbVsmXmLZ2G2QyHYwY8km5fyaBodfgc+g0kt7Oh1k+ZRJmr9uMx7MNoM3XwvhzEjia85WOobnDhBBCSN1QkSzSxef0qpoWpqo3WV1lW54rRssRq0q8Q2WGBiA7M6nMshfv0Ei74Iu04I0wGbxYuXOCyWHs8SWEba3x4sAXJVYJqa3h05RPhVQVZY1uxMZPmoHsn0tmOhwwYBBeBm/EyxuH0dxtJswXBqHFp3ORJ2NK2QKLZxJMSXquskf1dUYK9h+9CC25FEZ9JiAzLAAJW4YjMywARn0mAPJ88PREMOj6CVie6uHT0lf/QiDgo7n7QqUMi8YeC/Hq1nGltYIBOUwU1t2TiV9CS1uHW9O4xadzAQAJm4chN8wfIoEWMi9tB8t7g/RL25TXHr7sh6a9RiuXJSulRPlkOWk44qGllOX5gDsP23aUf63hwNBrWLTzKPJ7z4DZwiC8Nu2BJRu3gjGGj3bl4OSjAnxkzKMMh4QQQkgdpe7dSnE1DHUZmMu7JrFhM2O82D8L8RuH4MWBLyCOvIG8pEiwglzVU9AyE8u10oVih8abp7dRkPocBtafIDM0AAlbhiH1nDf0OjpAlpPJXUfdKiHlWUe5qijrM6kqjfYIFxQUYPny5UhOTkZ+fj5mzZoFV1fXsg8k1aK0JAy3b1+H0K1k725RC1/olfMqE229un1SKZlVXlIk+EIRhrhagy8UQWBojPen7eY+z41/CO0WZmjuNhOZoQHgqRuGI9THm6xMGKupJCdsGQ7tFqYw6jMBGSHblIKyyjWNhyxGauBaCIV63FyWIa7WaNZnFrfGcOGQauWEWnlJkRA0baVUhrykSIjzobqnNl650lwan0OnIRrgBWFba2SG7oZ+1BWcHCVUmiPs8YEAky9IcPgzPcpwWMMoPhFC6iKKTXWbuncroORqGMWHEJenN/l6eAjyZAwtBs5VGl0nL8gH36C5yuN5uvplLucEKPc0Z905hRYDC99Jit7rcuMfIjMsANotTLnrKP5ZXZlrCmV9JlWl0R7hixcvwsjICN9//z327duHdevWabI4jZKzqwf2H72Ii+EPsf/oRS4Yv85IKbWF78A+/xLr3xkPXoSc+z8qtYKmBW+EVJIDQXNTaJt2LrFmcMbbHteiFktRJ2ekFdsnPXgTdPh8tesZaxuboe3ii3h/2m4IDI2hpaNXvjWNpfmQmDlg25a1GOxqBb5QhILMZDTtNRraLUwhzxMj+48f8fLmMa4sry5vA69AUqKl9z2TZqpbJdsWVpoDQ6/BftwstHIaBPtxsxAYeq3EzyIpKRGy7DS8ChiHnD8uwUCb4d8cxvUwH/5MD8ceFkDGN8CUywIIvXMw+5oBls6eS3NhagDFJ0JIXUSxqe4JvXJeqacXQIl3q/KsG1xab3KR40f2wmjgAuX3L/cFgDQPTC4r8Z6VHrwJn34ypFzDlBXXLVb37lSQkYgmPUZyI+gU/1yRdZSrw/D+Llg6ey5mXzOgdyJSKRrtEf7000/xySfv5k/y+fxS9ia1qaxWyZTEWNXzU/LeABH7kZD8HFo6ejC0Gwyj3mO5eSU8wxZIDVoPVlCYSdrIaSJEnfoiN/4htHT0oG/xMSQJjwr3yZdAS0cPpm3aICkuBs3dpyPjsp/Ssk5pwRshF2fhxf4voGfRCzkPf4ZAiyH7Z/9S1zQuSvbwJvI6TIauUD5ffh5ajVgFaXY6sm6fxOs7p5H9+wUYGjbBnDlfASjZ0qsFhkkB3+CIQqvktEtyLFsxA2cv/oxFO49CNMCLawVetLMwAZjisgDNmzWDIGIXTn8mgKO5YeE5LkoAAGOttOFozsebAi3EXjhVIz9zooziEyGkLqLYVLdcDw/BnoDtMOxfeoKr8iTRKm2kXlnnYdI8CJq2REF6Ivee1apNe8yfv7Tcc3UVM1lrN1fd0ysQGiDz0nYYNjNGbpg/lzVacYnNml5iUhFlfSZVodGKsEgkAgDk5ORg3rx5mD9f9QLhing8HgwNDSt8LT6fX6njaltdKef0GV7Ytt0X6K+Q1j/UHwvmL4WhoSFam3XgAqQ48sbbpFqJEAgNMH2GFw7s8wf7eKrScGT9Ts7IefgzhG2tkffiKbdeb2HP8SawvLzCpFl6Bmg5fCV33eTgjWCMIev2Seh1dCgcupyeCC1d5Yp2WvAmyPPeYNnqjQCAXTu+RUraP4BAB+khW2Ds8W5N44zLfoBchhZvkz0UldFk8GKknvPGm6hfIIn5TanSnR3qD6FQiP6fDuXWJ1aUz9fBjGPbEBOfhI86tMHa9dMwevhIfNDzM27Ic9F1MMALvke+w+QRQ3D6cji+3nMYeTkZODNGXyn74YEheph7ORdjrbQRkSDDh+atYNhEM/8++Dy+xq6tCRWNT5WNTUDd+d6XpT6Usz6UEaj9ckpypdDT04OeHqAn14dQh8ypngAAIABJREFUWHhtuZ4ehCL15agv3/v6Us7qUJvvTrWtvnx/FZ04tg+G/UsmuDpxbJ/Su0IrhfemInlJkWhl1kHpngcPHaPyHaOs82jp6HPvVUXvLNNneKH/p0PVnqv48x48dAyEQiEO7PNHQUYi0n/cBONBi5TOuWT5+lLPWR6K8UhXRxfa+uX/mdfX73p9LXdDp/Gs0f/88w9mz56NcePGYfDgspeakcvlJdavLQ9V697WRXWlnD17u2JWbq5yq+TM+ejZ2xXZ2dmY+vlcbNvmC4mlE8SR12GsUGHcts0XbzL/hfko5RZLSdQdLiN0ZmgAUs95g+W9gZauPkQf9YXoQ0ekBq0vkTXaZPBiZIYGoHn/mYWVXfErrre5aN5K4X6LkBq4Fvv3+mH8pBk4dupn3L0dDv9t3sjOESPjin9h0i2j1jBymoj0H7eoHjKd/wbiyOswGbpc+Rdb/7n41mclcnNzVbZ02jsOwJgJo9G+uQQ6kEFbnIjs7GwkPI+D2XAV6/AlJOBw4EUs2LoXugKe2nnGj9PluPZcCs+gXKz+cjayX2vm34dhE8Mau7awRs5adRWJT5WNTUDd+d6XpT6Usz6UEaj9ckqkgEQihEQiAU/yBgUFhdt5EgkKZOrLUZPf++pUU+VsCLEJqFp8qk11+furuKyQ4lq56kbIJSbGKt2L54TP1a4bPHqYS4nzqqPqPGnBG8Hy3iAzLABNe40uzG3Sfy727/VDz94l548r3kuTFq3A5DJkv0znrr/v8Hml/VS9B1aFUjzKz0OutPznqy8xqbiaKHddjU/1iUYrwunp6Zg6dSq+/vpr9OrVS5NFISoUT+tflO0wNTkOrcw6wLmPM37++UcYf7asREto/oUNKnuMM0MD0PTj0WjefyZyEx6i+bAVSq2a6tYwLshM4iq7RZXi9Mt+0DE255JZFc37heN0bljS4KFj0LO3K/b4eeOnnwoXpJfnS8AXNVM77Ee7uRkKMhLVrim8fbsvHv/1J2Z5rSjXczRra67yOqamZli58yCM+Ln4fogAcy8XZoQu6hEGCucZ62sDw09LMMLdnYb/1CKKT4SQuohiU+0rvqyQ4vDn8i6XVN51g8t6x1A8j7ppaACgb9m7xNrF6u4l/dI2NHefDxgaKw3rLv4eWNYzUtVQQEhdptGKcEBAAF6/fo3du3dj9+7CTML79u2DUEhtHHWNqsB5/Wd/SCU5qiuMkhyk/LACPP2m0OJrw9hjgfKwZBQmYpBmp+PFgS9QkJEE7Ram0NLVV7OWryl37qJKsfFAL2SGBXAV4bykSPANmhcOnX75L/y2rodQKERubi7Cwi4DOvpoNXQFpNnpSLu4EfI8MdIuboTJEOX18fQ7OUP6R4baNYWbu83E5aD1AFCuyvAKr8lYuM4PGPCu11z8sx/WzJmIBd6b0KaJFtyO5eN9Ay1MPi/B4aHvMkKPDZSAMS1k5TL88ttvCAq9RpXhWkLxiRBSF1Fsqn2Kia4A5bVyx0+agT0B25WmkqVd3AgDoQ6uh4coVQbLs26w8eBFuHLeGx91sVFbkSw6z/SJQyAxc4Ak+g5e3z0D7Ram0O/kjKw7p8AXNeMq44qVVL5QhBbFOjCM3RcgMyygcFWPSqwBXFpDAVWGSV2m0YrwypUrsXLlSk0WgZSTul8CkqD1qpMpGLXG+9P34MX+WWjxqfJxRYu78/Sa4NWtYzB2f1dJTj2/QeXi7UZOE7lzK1WKMxLBZNLCY4O8oaWtozRHZtt2XwiYDHJtPRi/LYc48gZ42rowGbIY0ux0bsg0v4kJRJ1dkBd5FUIBX2Ul2chpYmHPc4EEP4VdKfUXVZGRQwZA/lIMn0N7kZiUCFNTM6yZMxFaAJoKtbBv8LuK75izbzD1ggTxWQzmJk0g1xIgeKz2289zMHWXPwBQZbgWUHwihNRFFJtqX2mJrpxdPSAUCrHTzxcp6YXTr5q5TIWgWO9qRc4rz3tTZmX0engIUpKeg58tVpqeln7ZD7KsVG7Y9fhR/ZH1tlzN3ecj49I21SPvMpKU7qsiSmsoqO6K8KlLP+Pb3XvxNCkNlqYm8Joyid6JGqEHDx5g8+bNOHbsmNL2q1evYteuXRAIBBgxYgRGjRpV6nk0PkeY1A9qsxTmS5B+2U85CF/ahmZ9J0OLL4A061+16fe13lZGFQNny6HLkHJm9bus0W8TYulb9uaWW1KsFGtp6yFh8zAIjFqDJxTB2H1+iXm9qYHrwKR5XDkU18YDAIMu/ZAb/xCpgeugl/gbPp/zFZxdPXA9PAR+W9dDmptTIsM1v0lLGA/04oL89fAQHD2yF+nJcTBt3x4rl8zC2IE23D2P6O+ilCF69JfL8b+HfyI7Dxj6/+ydeWBU5dn2f2f2NZNlkqAmobZValTcurx8IPtiQMKmItZdrKClSH2httBFJWhBxDRaoKKotCAoARIgyCJBodhWXKgNLn2LJIGSZLLOvp7vj5M5ycnMoCAq6Pn9A5mZ88yZk8lznvu57/u61vrod56WbbdYeek6C3dX+slNN2MxmVhZFFGIZz03JsJ9K19QJ30VFRUVFZUviU8rfx5xzXhWLC/FdGOJ4jWfFgymGlefmS/bVSYjnoEVjBacRUoxTmfRTBrL5yvKrh3dNvS1aTknrLw7FQ/gVGvE2vrDckvdqZZLr9+xmwUr11FfX0dmRgYW0c0LY3UMKLApEgQApStfUAPkbwDPPPMMFRUVmM1mxePhcJhHH32UV155BbPZzJQpUxgyZAjZ2dkpx1IDYZWU9CylSapSaLRgLRwslSM31yPojFgvHkL7/rW4Ni9G0JtTHpesHzjidiFodIhhKQgWIxF8h16nY/86BKMF+5XXykGxa+sSQCCt3w1kDLyFIwuLk4tfhQOyB7Gpd9/UvsLhAA1HD1P6xHwWlzyIzmwj4vegsTgUCteurUsQwyEibhctRz9JWhL0wNzFaIO3M2FMoojJ5J//ikOH3mPjZIucCb6p3M81q7xU3mThP60iRquexvpGBhQoFQYHFGj5sL7pVH+lKioqKioqKidJd1uh7kJX06d3KXZ/FnukZOM++eRjOMfOTmjRMtf9Q/Hanmsyy+WjEd9cl2I94+fAgb9hHzmDqLeV/z7/M8LN9egcucQCnqQJjPSrb5F9h0cNv+akrk+ygL5t3xq01nQYMDWhXLrvoM8WDK/fsVthP9m+4nZeKNYlJAjuWLocIRbiuTGahABZDYa/fhQUFFBWVsacOXMUj//f//0fBQUFOBwOAK666ireeustioqKUo6lBsIqSQUOAEVw17ZvDa7KRYrJ2rV1CbaLh+CrqZZthur/eDv+//xDLndu27eGpspFZHc/rqoUMehD78xXTJzemj20vbGKnEm/VvTZmL99Fe53tqLRm6WguLMPJmPQ7WitGTSufxhvTTWIIsdWTCf96psVfcOCwUzU14Fr6xKco2elFMmKB+cYbQixGJbLR6PPPI/mV5+WyqfbGxTv27ytjJzzvsUzSxcTimlpWDsPfVaepNg4fAYPL1meNBA+cPA9Nk42Kyby1RPNjF/rY29tlDRHGqaiB/Fv/HVS8ax8pyq/r6KioqKi8mXxWfx9P6toVs9xD73/Lts2lhAL+tBn5ks6JR++zs3dguykAlcnyO7mnnc+jUc/IdPton3vXxRWkE0VCzHknE/zpkeJBryY0zKI+T00b12CPjMfa9+RVL9R/Zlav+Ik2yjwvLOZ7PFzk5ZLL/yMgfCClesU9pMdbW3MqNJwyBXjIqeGuVcbicREmts78IVhRpX02JRL9WoF3ZeA97/H0RqNp33c1mCA++67T/558uTJTJ48Wf551KhR1NfXJxzn8XgUdmBWqxWPx3PC91ID4W84qQQOdESxj+wqMzY4C/CIIo3rH0YMBxEMJsRQgEDtwS5v3+b6hHLnuL1R08YFxIJeNEYrsYAXjcmKqaCvIkhue+PPOEfPUlonFc+hsXw+iCJRTzPatGwQRSLtTbRWr8T8nR8i6E0JO5tiLIrO7sS1ZQliNAyxCDFBpOHl30I0Igf1EbeL9n1rpB5hWybpw++Rjtu6BO/7u9AYLRANce7UpQjarj8XMRoh0nacqwbdyLYdVQm9xI4BP6bxSF3Sa+4OikltktxBuKkihn7g3RjzCmkIRLlpU4TV4+gSzyr34xMtp/17oKKioqKiopKaT1NQ/ixZ42RMnzmXiy65XE5ImOv+wc09guxkPbjOopm4tjxBc1WpItCNv+efX1hO8741ilawqLcVjcGM/+O/4XD24qe/ms+K5aXYix5QBNOB3pedVH9vso2CWNCXNFt9Mv3H9fV1cpbdW7ObHJuGsiKTvCa6faMfX1ik4sauCru7KvwAXFeoUyvovmC0RiO121877eNeeOMkysvLT/o4m82G1+uVf/Z6vZ/qS64Gwt9wUgkcNK5/hHR58tlD2+svKoI919YlpI+Yjs7upLmqFPN3foAYCRNpS+wJTu8/hY7969A6chQBa1PFQmK+drkfGEjh6+tHMFoQdHqco+/vOocti/G8v4vc636boH7YuP5hNBYHsUiQ3OsfUrynGPYTC/poeOUhNAZzUkEs5+hZNLz8O2IBL4LOlHTH1eHsxYEDf0voc84qmknztjJ0RguvVGznuuKRis9kN5I002s3QrsniPbVxTRXLsZmEGjR5zJhXT3uoMhFTg1Tr9Cz4m2fqh6toqKioqJyBvFZssYnOjb+uniV3hMLfilX6aUqu456mskaPUsW/XQ4eynec3HJg/K6Kr6W6x40P/b73xH1tFGQImA9GUukZIrYJ5sh70leXlflYGT/KtZNNCmq6Z4fb+buSr/isWeLzcyoCtDLJtAnL3VvqMrXj+985zscOXKEtrY2LBYLb731FnfdddcJj1ED4W84KUWwwgGC9TVEva207FhKLOilZecyTAV9CdQeJOp20brrT2SOmI6lcDCeg9vJHjubhlce4tiK6XIZsaPfZLTWDASDGVvhIII7n6SxuQlHVjb2y0bS8fYWcibMxZhXyLEV01OWLGv0JpzXPqAMeMc8QOP6h5MHz5EQgqAhZ9yDCRnm5m1lxMIBNIImYcysopm4qkpJH/BjNEaLrCzt2roE68VD8X+0n3BLHYLBjEmno911POkNJNJ2nLR+N/DA/GUATPj+D+Xn3UG4qdzP6olmRY+wPwTnpMHz4yzd7JPqmfoDPfOHdtliDDtfp5b7qKioqKionGGcjO9uMpJV6T311OMIOiO1iyegz8qX2q8KBxGsr0FjtNK8dQkao4XR425UWDoOHjaGFcuflNdVPYVCTb374rx2No3ljyRde2mMZp566nHSi2adkiXSqWbIu/OrO25g9lOS/WR7cxMDCmyK5wcUaDncJiY8dsgV484tMR6877bP/F4qZy+VlZX4fD4mT57Mgw8+yF133YUoikyaNInc3NwTHqsGwt9wUvW0OJy9aK1cSFSrJ3v8r+R+33jA2z2DKkajZI+dje+jv6IxmMm6ZoYicxwLhxBDPsyHKlhd3CVkcFNFBe3BgPze6VffLPfxKsYP+oimKLERw8HkwbPOQNTbSsPaeWhtmaDREu1oQp+ZR6RNshFIlr025hUSbW+kZecycibMk8cN/fejhM/u2roEwRilbd8auQQ8/v5aRw4ZA28h0PsySkr/xIRVP6R8x25KV74gjRcVGbPaRyAiZYgjUShIl6yUuu9srpkk7WzOH9p1jqpgloqKioqKytePZFV66UVSxjdv6lK5NzjkqsVXU03miOmym8WBvSsSxpt6z/1yMJpSKDQUoHFDiZyUkLVcNHrSi2YlVAx+1pLpE2XIWyKf7XrE3TYWrFyOVS8mVNM9tCdImhG0D3fIPcO9bAIOk5YH75uhJgy+xuTl5bFu3ToAxo7t0uMZOnQoQ4cOTXVYAprTfmYqZxU333YP7u1lBI4cRIxGCBw5iHt7GVPvuR+dTiv37ApaHf6P95M9drb8czyDGgt6MOYV4q2plsuE4887R89Co9VhMwqsLtYw5Hwdeq0gCUQVa7CbuiY0a+Eg0q++hcb1D1O7eCJNmx4j4mkDQNAbCdbXKM5dsk8y0lS5SHH+TZULQWsgZ9JvyBp9PwgCzqKZFDxQTuaIaWis6VguGog+Kz/pmLr0XohBpaJ1oPZgwmd3jp6F1pyG553Nivdvriolo9PiyZhXSN2RWsp37Oaxp8t4eoiH4Dw7r1xvwaqH3g4BTwg0GvhPa/Le4UOumOKxvbVRtdxHRUVFRUXlDKN61xam3lpM8bC+TL21mOpdW07q+MajnyQNViPtDV1rj6KZeN7ZIls6xl+TzG5p8LAxUgZ27woEnSH1micSorF8PrWPT6Bp4wIs3/kBMX9H0nNp7CyZ/iyfc/CwMax4sYKKXQdZ8WLFKWXLJ40YwoHVS3n8wdncuSXG7sMRwlGRea8FePadMOU3WAjMtVNWZOLBXQEml4d5dPbP1SBY5TOhZoS/4aTasQPwtbfg7DYJxncTvTV7aN+/lnBzPfrMPMRwkNrFE0Ak6fNRTwtekgd5nmCEzG6P6exOEDToc79N1N1M2g/HS+XIzXWJ6tNblyCKIjp7ltRnHA5IPngi5IyXSqKPPXtvogDX2Nm07FyG4/9NThCZiPctCwZlX3CqndRI23EEvZGGl+Yi6I0IehOZw+5WqFbn9y6gdOULPDdGI+9kHvdIpTzazq2oXKtAsyDy0J6gogx6b20UuwF2H47I5dJquY+KioqKisqZRSrxUUheSpys/9ae4UzaXhb3+AVp7RELeBVjde+97TnuVVf9CL/PA4I2oerOtXUJ5m//AN9Hf03QS9HaMpNW3NkznEnLt59Zuhh3q+uU/YI/jXhg+9MXVvFBbQMOk4b11yur6J4fZ+aOKp0aBKt8ZtRAWCVpT8vUW4sV3rsA+qw82vatUdglBetraKpcRMbgO2jZsYy2fWvw/us1xUTbVLEQS7QjqUCUzaglcORgQnAbaTmG/aqxivdq27eGxg0liCEfOkcvxHCItB+Mx/12JYjgvPYBrIWDFH7CqQLYcHO9HKy27FhGuLlO3hUt+N8N8ntpzWnSTqzBnFAC3bTpMTRWB9lj5yg+q6vqKVr3vEDU3YTGaOGO665h5V/KFb0tc3YEsBikUmhZ/XCTnyffDDHsfJ1CJfqKvpdz3+5jskn8g/epJvEqKioqKipnEqnER5OVEicLmsuefBRRqyWruyjo1iXEwkGyht8jHxvP4ra8tgLX1lKIhtEYzVxzzbik475auQhRFMmZOFfSfel0+dA5ckEUCXzyDrbLRsmP67PysBQOxltTnRA4u7eXoddosQ9PXr6d/0D5SfcSnwwTRwzhtknFuDvc9Bo4JmmCpc7lPq3vqfL1Rg2EVYDEHcSGo4clJcJuGVPzBf1wv12p6J3tnmEVQ37cByqxXzVWMaHaLhuF5+3N3LQxxOrx3ayA1vtxBzV4Oi2ZNJY0RBF01nQibcfxf7RfIeyQMfAWzL0vo3H9Iwg6PZlX/wRLn/507F+HxuKgff9arIWD0FozaNu3Bv/H+1N6C8d3V+Ovb9m5jLQfXUfL9j9S+/gE0OrRmCyKfuemykWApILdtm8NgdqDideieA6NG0oU6tZrq/5AjsOk2AhoC0py/z13Motf8lH8kg9vCOwmDdeNHs1js7q81FRUVFRUVFTOHKp3beEvq56hof5wSvXlniQLmmN6SWMlwQWjfD5aawZiNCKvRTKHTkVrzaB5WxnndvYO76pawr591Yk2S2Nn07j+EYx5hQhanbwWEqMRuZrPW1OttKGsKiXa3ojB6iCws4zG5ga5YvCJBb/E8Snl2yfTS3yq9MnLZm+tJyHBoraOqZwMaiCsknQHUbOxBJ3dSfrAW7s8gg3mhN5Z6Mqw6p35hF11igm1bd8a3G9XIgb9NER0FK8N4A3FsNusuIN6IIwuLQfzhf3wHHwV+2Wj6Ni/DsFgJtxcl0IgKwCAa/NidG/8GY3FAUDYVcfR5XcT9bTgOfiqIlPb3Vu4qXIRtr4j5ZtK3P6p7Y1V5Ez6jaxg3fOGlD12No3l8+l4cx0aoxUxlPxaiCGf4rjAhQMJvP0yt2+C58dJGWBviKQ7md4QNLyx9bT9blVUVFRUVFROH90TB/YMJ8GoSHrRLPTeZXIVXVeLWB06k43qXVsUQWEyx45Ie0OKNY+/K7mQmUfM24a1cBBiNKIIPtOLZtHw0tykQWrcCSRBWNRgAcDZU026aCaN5Y9gNpuZek+ip3GysXqWb5+MX/CpMPOO27jz6TKeG6O2jqmcOqpYlopiZzI+odquuJamyoVorRmcc/sfyJ08HzHoSykwpc/Kw1TQF8FglidU34f78NVUkzNhHgX/u4Gc6x/Cb8hA1BrxiiZyrvsdBQ9sIHPENHw11dj6jsL7z11orOnkTJyX9L3a9q1BY00nc/g0Ch4oJ+uaGQg6Pdo0Jxqrg6xrZqDPyid7bKJoV8v2P9K8rQxrn/54a6qpfXwCzdvKcAz4Mf7/HFAIg53ohtR7TiWxoA99ZoprkZkv/+yt2Y34zno6QiIaAW7d4MdU4iat00u4O3troxRkp52OX6mKioqKiorKaSaeOGDAVPIfKMcX08rKynHdkdbXV9H6+oud65QNZI37JUuXPikLSlXv2oLWZE1YP+gcuSnWV/mce9cf6T2ngswR09A787s9pww+Bb1JMYa3Zg/HVkwHoHFDCa2vr5KFPV2ViyAaQUzpyhHANHyG4twhuciqa+sSHP0mK877ZPyCT4WJI4bw4H0zuG+3DVOJh/t221SlaJWTRs0IqyTdmUzvP4WO/euk/tmWOjRGCxZHJroL+yUKTHX2CLfvW4MY7sqSJvWsG/OAVDqcxL+3aeMCxGiEnEm/UdxUur9XstLseOlQzkTp8XBLCouAcBDEGJYL/x8ZQ6d2Zqs307x1iSz0FUeflZd8x7MzyNVn5WG+INm1WIit7yhACoJ1bzzNuhuM8m7lXRV+/jzBTCQmMmW9nzWTunqEb6sIM/dnahm0ioqKiorKmUjPkubum+bWwkEEjx7CfaBSzuLGPX/jpcIAS5c+ieXy0biqShXlyLGgL6Ent2cFm2vrEtKvvkUOPjMG3S6fW7C+BrvdTlvVEtKLZhFxu2h7Y1XCeB3716F15GA4tw/BYx+kFMXSZ+XLZc4rlj+paJ8bfPVgDuxdQd3RT0jLykUbDSvKt0/WL/hUmThiiBr4qnwu1EBYJaWXsN4pGbfHy3sCooj49mbsV14rB8iCwYwYidK89UkQRTmLa+rdN7VnXYrdx1jQCwgJN5W4QJY+M18uR+6uTK21ZoIYo2HtPPRZeSknda0jp1updx2CwULWyHuxFg7i2LP3Ko5x9Juc8oYUf77t9RexFA7udi0sGHpdgPdfr2HufRmR/atYV6xR9AE/Wyz5ApcVmQhHRSau89MRFOmTl8Pcn6kiWCoqKioqKmcqPRMH3TfNvTV78P/fP8iZOE+hvgxg6dOf2vrDPLN0Mbo+Q/B/vJ9oRyNNGxcQC3jRZ+WTOfwnAF3taDoDtkuH4/94Px1vrkNrtCBGYzRvXYI2LQcxHCLkqqV9xb1SwsJg4dJL+nLog0M0bysj6m0jZ9KvE1q8XFWl5E17lmMr7iV77Byi3taETf3mqlLSu9lANriOY7qxRG6fq+4MdOMl0/Fy8Z5+wSoqZzpqIKzCzbfdIxuudxdKsHznB7S9/mJC9rfj7xsgGkbryMFaOJiOf2xE58gl6m3DfGE/2eZIn5k8qyoYLSl3H+P/j99UvDXVUhCcJQXlrdUrqf/j7QBkF8+Rdzzjvb3xHdOmTb8ne9wvFGrOAGIsSubwaTRVLCRjyJ2yaISj32RcW5bgHCMFvlprBrFwULJlCvnR2rMQEOjYvw7foddx9J+CpXAw7rc3IwZ9CEYLYihI6NgHiOEgjRtKIOhlQIFdca3jvsC3b/ITFSGms+B9dxvuDlXlUEVFRUVF5UymZ+Kg+6Z5+18Tq+CyimbSsnMZWmsGuvReuD0daN5/TV5rxNdV5gv7yesRa+EgKeNbVUrmiGlAl7CVgEDBAxsQtDpadizDc3C7wlby/cpFWPuOImPgLQoHjTjGvEKiHU0AhFvqZAEtUAbgWdfMUAiM6tJ7nVANO5n7iIrK2YAaCKsovIRr6w9LWd6wH29NNdnjf5WoEL1jGZkjptFcVUrM70ZjkJQO40Gp8dw+UhY36Evq/Wu7eEjS8mprn/4Yz7uI5qpSWbo/e/yvlDYCkRBao1UWskrmE+wcPQvX5sU0bpiPGPSjS+9FxpA7JaGsioXEQn6IxSTP4k6shYMIuWrlYwS9CY3JivXyIrzv70LQaMka8/OEwDqeUfbW7KFl5zLsRi0d7UHSzHpCMSGpZZTDCI8NM9HLJlD8ktILUEVFRUVFReXMpGfiQGvNQBsNE9z1FOHm/6YQE62TM6xtb/w5uRDnhvmYe1/Wtd7ZspiMwXfK40jCVmZMBoMciAdqD5I9dnaCQnTLjmVkDLzlBC1eeQSOHEQwdCUlrIWD5AC8aWOJUqW6M3HQ83N90WJYJ0P5jt2UrnxBtpmceYdaYafy2VADYRWgKxiOq0cb8wqpXTwh+aTeUi/vdDZumK/o2RU0Wtre+LOcJY152+TSZkFnJHPkvdguGYr3vIu67T4asX+/GPdbmwjUHsT8nR/gfnszORPmJgS4zdvKFD05qcqvo54WAHTpvRTWSdnFc2jeVoYYDiYE4573XiVz+DRslwyVxxKjEdxvVSSWFxXPoWlD182iddefyDEEWV2so77DyEN72vlPWGT8Wh8zfmjgt4OMco/w06PNTLlUTzgq4g2d7t+kioqKioqKyhdB98RBvAx4xv2/ZOz4G5k8YUjSwFNjtJI+8FashYNwbV6comXML69vpIq0gCIYba4qxX7lWLzvbMa9vQxGzlCsf7q3iwnmcGSbAAAgAElEQVQ6A96aPTj6JeqsNFUuJOZtp2XnsqRJCff2Mq4ZVSz3/+ac9y2MQoz2fWto3vok+qw8HP0mo7VmfOFiWJ+V8h27eezpMp4bo2FAgY29tR7ufLoMQA2GVT4VNRBWkekpAtG93zdOd5XC+OTdfVK3Fg6SemEen0DB/Wvx1uyh7Y0/Ewn6QNDIWdjuu48tO5fJ4lzmC/rhee/VlH3EkfYGxS5nqh1PXXov2Vsv3qNjLRzUOcZxQMB57QOKYFxjtCqyxPGxxHAgRU+zj+ZNjxIJeLDpRVZfZ+G4R+S31UGeLTYr/JIXvBHi2xkCJUNNTLlUD0jZYavh8/zGVFRUVFRUVL5MUpUB33zbPTz11OOkF81SVLJljpgub8bHlaHja5b4Ggkg6u8AUURjsmI45wKpfzjoQ5+VR/rAW7H06S+1ZwV8+MvnI+ik7HDU25rQxhYX1bIUDu7SWcnKx9pnAL5//w3zBf3wf7SfSHuj1AIW9pN73vkJvb3Vu7bw1FOPk3XNDMXY2miYGff/8ku42krKd+zmDy+s4oPaBjnzW7ryBZ4bo9RjeW5MhPtWvqAGwiqfihoIqwDSZNfTDN7Rb3KCqmF3AQWpVMeUwpvOBCgDYzEcwrthHo3BGFYD+AQrsWgEoiGOrZiOxuLA//F+bJeNwn2gkrZ9a/B/vF/yzutUadY5chW7nGk/ui5B1CqupBi3Tor36FgLB8m+eTp7VkIwbr6gH02VCxX+w40bH0MwmlP2OmeN+yXu7WV4W/7LgAItVyz38myxWTEhr5lkZsJaH+EY9LIJhKMie2uj3L7Jj8Om7CFWUVFRUVFR+WLo7gGcc963uPm2e05bb2t8nBXLn6TBdRxBbwSNVpHZjfo75JaxZKrOrqpSzBf0w1tTTSzgpfcvKuXxA0cOglaPxmSVj3dtXYKg0SU6dIyeReP6h0EQEMMBWWfFWjgIMRbD896rZBfPUWSC49eietcWnlm6GLfbDYiyk0fPseMq2F9Wb3CqzG9tSyCpHsuH9U1fynl9owkGED/56Ks+i8+FGgirsLS0hG2vVgBwbMV0uZRY7pvt3C0UDBbsV16LpU9/qY+kchFiKJAQLLuqShFDAXn8YH0NWksaTqFDYRd006YwjSEjGaPuQ2d34tqyhLCrjlg4iKl33wQRiKbKRcRCQbTWDBwDfiyVEbUdB50BV1Up0Y5Gufw6vvsKneXcrjqOLr+baNCHGPRhvvLaLjuCqlKshYPxvv8ags4kC2QJBjOCRovtyrFJe5rFSFgWjdBt/DV7a6MccsUYUKBVXN8BBVo6giAgcneln8NtIuenC3hCAgtmT/tyfskqKioqKirfYOIewPaRM2T146VLnwSSB3OnEjT3zBbHx6itP4zObEMM+hAjYVxbnyTmdye0XTk7N+6dRTNpLJ9P4MjBBG0SRJHG9Y8gdpZPRzpcKS0jc28sSVCwDnzyDsbzvicrVmtMVoz5l/DnF5azc9tGDv7zPWkNZLSk9hiOhGDA1BNev9NNqszvpJe1SfVY+uRlf+Hn9E1HYzFjv6Lw0194BqMGwmc5p7q7GT+uof4wgtGMGPKjz8rHfGE/Wvc8jxiLorM78dVUY+rdl+DRD7BdNgr/R5KMv2AwY71oMP7DB7AWDpZLjPVZeVJQ+c+dit4Wm15kzThlpnT1OJhQoaXjb69w7l1/xDlG8gOO3wh6ikBkj51N4/qHFYGq7fIiPO9tw1k0UwpOw4Gk5c3xz+Z+ezMA7gOVdOyXPocY8uM79DrRgAeNwaSwPmh4aS7p/adgcBZ0fcbMPGLeNnl8Y14hjYEYN28McX56coGs3g4BT0hUnJdWqwyYVVRUVFRUVL4YerZ/JVM/jnOyQXMqBg8bw6H33+XV1layegiHipHkbVfx3l8x5Me1sYRowIcuvRf67G8R+u/HCDq9IpvbWD4/uWWkLTNBwTqeQIiFAwox0qbKRTR422hqbVWsgRo3JB9bn5V3wuv3RfBhfRMDCmyKxwYUaGnzR7lzS4znxkTkRMudW2I8eN9tX/g5qZz9aL7qE1A5deITNQOmkv9AOQyYylNPPc6PrxtK8bC+TL21mOpdW1Ie58//ARprOjkT5lHwwAYyR0zDc3A70Q4XLTuW0vDy7zB/5wdkj3uQWNCL9587CTfXgdYAsRied6uI+tx4DkoiUwUPlJM5fBqeg68S9bZRu3ii1P878FY6OtzJM6Xt7YSb6wHkib/7jaA70i5kEI3Jiu3yIrQWB553qxD0RlybnyDmbZMz1IEjBxGjEQJHDtJcVYqpd198NdXkTJhLwf9uIGfiPLSOHOxXjUVjTSca8CAgysF3vKxal96LYH2N4jzEaBiNxYFgtHBkYTHHVkzHZLURjEnB7pT1fnYfjhCOiuw+HOH2jX4iMVh3vYVHhpi4yKnhcJtImj7CI08tO83fChUVFRUVFZWeNB79JOm6ojGJ+nH3oDm+HrB3Bn0nQ/WuLWx7tQJn59rC9+G+zgo2F4LelLC+iAeZwfoaLI5MnM5ccm8s4bx7niHc8G+puq7TKSN+XoLOgGvrEsW6x7V1CSAkfNZI23EEgzlhrZM9dnbSx+1XjqWpcmHCmsrRb/IJr9/ppnzHbhwmDXtro4rH99ZG+V5+Dg/eN4P7dtswlXi4b7eNB++bofYHq3wm1IzwWUyy3c30IklZOf+B8pQ7mPHjUmVd4/ZITZWLcL+3g4jbhaAzEIuGEUw2NAYTztGziLhdtOxcjq3vqM5saR0ao5VYwItgMGO/aiwZA28BILL/BfbWehIypXabBb8xC+iyB+h+I0jmNazLOBfvoWpFFtv7r9fQWBySNVI0QsNLcxH0RjRGGxlD7qB9f6K/n1R69AigQYxJvco9b5KO/lNoLC9BY7LgHD2LqLuJ8L6VdPjasFmMcNlINJ+8SYfXzbkZAkXf1bPuX2FFCXQgItLkg/qOWBIhLQ9rt25n9IB+p/nboaKioiLREgGb3UyGxQOolSgq30x6egCDtK5Ipn7cePQT8pMEzSdjGVS9awulT8wnFvDRsmOZFAj/+29yT3DbvjWJFpOdrVpNlYuwGfU01B9Gv2MZ4ZZ6EEViSUqVY/4Oskbfr6jMS7/6Fpq3LlG8Llhfg8PZi3bX8eSJhpA/4fG4kGk8kyzojWhMNsWY9gwnU28tTqhMPF392PHe4Hu/r+WuCr9iDRXP/E4cMUQNfFVOCTUQPotpOKoUt4IuZeX4bl6yspX4BJ8q6xq3R8oeO5vG8vn4P/4buvReRIM+tCbJwzfqbaV9718QQ75upcN/QmO0EAv60Focchlyev8piBcMZsr6l3v0CEdwh3RYv3OJ1HNcsRAxGqGxvART70sTbhDNVaWYv/MDvB/uI2fCPMXj1ouH0vH3DWiMFpzXPqAoPwoePUTYVZeihyaAxpKOGIiARsexFdNlZWpHv8lSmbUgSkGwtxndvqWsK9YwoMDOQ3uCPPvOq6yeaGZAgZ29tVF+XO7n7qsMzB9qkt9n9+EIE9b6pNcnEdL66bJn1EBYRUXlcxMPeHtiAwoLIqSbtBiIJh6oovINoKcHcFwkavr0+xNeezJBM8CObRtZsbyUhqOH0ZlsRPwetNZ0nON+qSg/tvUdKY8ZTxQ0bihBDPrQmKREgv+j/cS8bXR4QWtNJ3PENIx5hRxbMV0+j+7npXPkorM7OfeuP8qPxX2Ce/YY20wGBKMl6WdLKX5qtBAL+0nrdwPp/afIAXvIVUvg4KsIOj3mAVMVJeSH3n+X6jeqT6q0fP2O3SxYuY76+jry8vL51R03MGnEkG69wQYuztYyoyrAIVcMh0nLo7N/rgbAKp8LNRA+S6netQWNIbmacdzeCKRgr7b+MGOHXorObCMS8KAz2Wja9JicfT2hPVLYT8H/bpAn0UibtJP43+d/JqsxB+traK1eiaA3JEjsu9/aRMebL6PPzKMjoGNChYGO9nYcWdnoBt5CjjWLxvUP4z20B9CQe/3vaKpchP8/72D+9hVyP7DGZMVaODipgXxW0UxadixD0GjILp6TqG64oUQucU6WYc4cPo2GVx5CY7AmnH8s4JN3STue/wnriruEGjZ+EGH1RGVg+5eJZmZUBZjfZUUsi2V1BMWk5eEf1B4/Dd8IFRWVbzLxIDge8PZEcmpTg2CVby7JPIB72gXF+bSguXu2My0rF3/AT8a1cyjo1rfrTFFxFw+AoTPj+ubLIAjkz3wJkILY5m1lEPSQ1W2M9Ktvpnnn8gSnjKi/A9eWxTjHKJMAtouH4NryBLGABzEcRDCY6GhpRzDaEhINTZWL0KblJH1cDIewX3mtfN7xirrmTY9itdowDU/su96+6VGyxv3yM/VjgxQEz37qRawjZ8qB8+ynJHGv7r3BUy7VM+VSPeGoiKnEowbBKp8bNRA+S3lm6WJEjU4xIbbtW4P77c2IIR/Hnr1XNj3X2jJBq5NVj9v2rcFz8FXsV16La8sSnGNmKbKr3e2R9Fn5XT0kxXNoXP8wR5fdRdTbgjGvULJY2rqEWMCTQmL/EXrPqSBw5CANL83FMfV50rVdXzsxGkGMhMidPJ/G9Y8Q9bai0ZuIedsI1P4T+1VjFTuQ0Y7GFFnsOhBJUe7jI33kvcmVn2MxGtbOA40OQaOlYe08ORvsHD0L1+bFaOxZBOtraG9WCjWkUog+5IopHttbG+XbGQJNPjGpkFaaSf0zVFFROXW6B8E50WPovakD3rA1/0s8MxWVM4tUHsDJXgfJg+ZkQlqerUuIelsRtDqi3takZcbyWqUbwfoadI5cxEiIYyvuJdxSh2CwoAGiIWUZtGR9FKV5WxmN6x/uDG7NmHr3JXT837KSdLw1TZ95Hr5//42cSb9JKL12v1UpZ6IFowXbxUPIHDGNhrW/7hIk1RslC6hYBP9H+zE4C2RHDmNeIdGAl46AF0eyykS/J+nnT1VavmDlOqwjle1rgT6D+OWiJYiiyEVPe3hkiIkpl+oBVRVa5fShrsDPUtxuSXY/6m2VJz9J+GquMqPZKdWfe/3v5AnG//F+ssfOIeptxRMJdfV+GM3YrxyLGItydPndnY9ZOPL7seid+RjzLgEEot4WBL2Ztn1rMDgLEMMhxHAwZelx4MhBGstLEPTGlBno+Gt7msI3V5XKk2/cTiB5SY8FRDH5+Jn5kofw0UOysbzWno0Yi5Iz7sGkXn7NVaU4BvyYqLeVjCF30lxVSprDwd7akBzIXuTUJA1sLXr47h/c/HaQkbw0DXdV+HlosJHbNga4qdzfWUrdWR6+MUy7X83SqKiofD7OcZpIN/nRe6OEw5akr9HrfV/yWamonL10D4Ybj34iC2Ul02dxjp5Fy85lWAsH0b5/bcoqtJ7lyvF1GqBo62qrWoLVak0sg7Y70aefw7lTpTJob80eWl9/EeeYn3cpPW96DM/B7Yghf2KComgmTRsXIEb8aIxWRAHyZ/wFoTNBkTv5ETzvv0bztqcQ9CaFOnXcfslaOEhRJp7sc+rMtpMqLa+vr1P0ZHtrdmP+1wZWX29gQIGZvbVRbt3oJxITyUvTqKrQKqcNNRA+SxHDkuy+oNXR+vqLCEI6WdckTsyuzYuJ9tiZjPcGH33mHrLH/QJT777UL7urc5dwU8Lk56oqRZdxLr6P95Mz6deKjCqiSPa4X+CqKk3Zc9Lw0lxJaVogoaQnnoGO96H0FLSKl19bCwfJYg4Jmd2KhYiREGk/nJgk67sQ47nfAyBQe5CcCXMx9e7LsWfvxTn6/m7/n5XUZkCyg5J2QFt3P8uU9X65z3n893QJge2U9X7SjQI3XqJn9o4geg0sHGGil02gMFvD+O/pGL/WjzsEaQ4H7rCWLGdXP7GKioqKiorKV08qCyVf63GFPou3Zg/tf11LuLmOY8/eS9hVR9aYWUnXKraLh0jVbe2NUrZVZ0hIVsSFT10bS/BtegyMViLtDegcuUT9HdivvFZ+b2vhIEKu2q7sbqeYVcbgO3BtXpw0QRELeil4YINURXigktrFE9Bn5WMq6Ivv4zeJBTxJW83ibWghVy2edzYTC/pwZPXCX7mQjLFda0b39jJGjryW6u1lJ+zHzrB4MHiPAZDpzFasIcOvP8O68XpF69mL480Uv+Sj4JxePHjfLWpZtMppQQ2Ez1Iczm67jbEoEXfykuGotwV9Vr5igtFn5dG2bw3R9q5joh1NpPefgvutisQ+284dxOzxv0rq62vMKyRj4K0JQW5T5SLsVxWT3n8KdaU3Yv/+ODzvbpOCc08LuvReOAb8GK01QypTTmHcHrdXCtbXoHXkIMaiclmQ1pYpZWy3LpFEuYJeOeurz8zH1ncUnoPbaX19lUIcLNX/u79vpO049iulHWFr4SC01gxcWx5nQkWMjrY2cjMsuAMhbtng55hb5NsZAotGGPmwOcbT/wjREQSHEf7VFGXua2FKhpq4rlDHgr1hCuZUStfxyEGir/3h9HwpVFRUVFRUVD4Tn6ZqnMp3OLTpUXlN5a3Zk1DJ1lS5iHDLUdIH3iorOescuQhaHZkjppE5YhpiNELt4ok4vl9M9MA6GtfOlbRT+t2CtXBIZ+mxD43FQXY37ZKWrYsJHHyVQO/L5Me8/3oNYiK69F6dYql6Wnb+CW1adtIEhT4rH9+H+yRLyU7P4HhrncZoIWfSb2hYOy/FeqyO2EE32eO7qg8DVUvwb3+SxlaXooT8oksu58XO0nLned/ilp/cT99BY+RWjpxoLXqivPTqO3h8ASLd1pAd7R0MKLAr3n9AgRZvCA5UvIy7w/0FfjNUvkmcEYHwe++9x+OPP86qVau+6lM5a5h6z/089dTjpBfNIuppSakCiNZAxN0s2QkZLVgvGoT5gn64365UlO5obZkcWzFdzjR3x5hXSCzgTVH6HCRYXyNnTeM2SoJGh82owb1/LbEDayGmR595HpnDf0L7/rVEPS3EAm6atyxBMFowFVxKsO79bjuTl6Kt/QftzY3YjTraXn8Rz792E/N70VodiJEQuvRexII+BI1WDva7Z33jmHtfRmP5fARdV2l2d3umVFZNgt6I5/1d+D56E+ulw/DVVJMx+C4sffrTvngCWp2OXnaBBq/Irlstkgr0P8Os/meY8hssiizx5bkaplyqZ/fhCI6sbMU1rGtqRB9qOK3fjy8CwduBPuT/qk/jS0edn1S+juj1PrmE+kTzz9nyd3+2nKfKmUGqbC90lUSnslCK+D24O7Od7X9NtGbMHjubxg3zyZkwj3Nu/4NcAp0x+A55nGB9DVqzHdM/X2bNjfH1goebKp7GC2itWWhM1oTEROboB3BtLJEtIsWY1FqltaYnin0GfQkJCldVKRkDb02wlPR/vB+tOU2uLEy1LtKaLAkiYOlFs2DvCv7yymuKa9V30BiWXXsd53RWvW2vLOeRn1zDf2qPkut0YBBE6po6SDNqMF0+Cb2zt7xxYDOQtPXMavh8v3cVlZ585YHwM888Q0VFBWZzouWDSmq6964IOiNi0IerqhRnt13Jxo2PoTGaye5WstJUsZBY0A/REDGdkYZXHoJICI01HeeYn9OyY1nSyU9jknpVot5W2vevJdxcj9aaAVoDDS/NlbK7/aeQOXyadAPQxUuI7bJVUuNrz5A5fBqOftcT2b+K9uZGbEaBaMGlhBsPkz3+VxjzCmnftxrTP19hzUSTfPyU9S/T7teiMdkUk31T5SKadywja8Q0Gjf9HiJBheCVXFId9pP2PzfIiohpP7pOvkF0/3/3m0jmyHvR2Z00VS6i42/rseljNFcuIrB9MQIaIj43z0wyM3yVTxbNKnkjuUXSxLU+5r0WYMU7YXRDpiuube/zC9BecOYL2GjNFrT+b1aPoTo/qZzp9LRMCqNN2QscF8qK/6v31sGnzD1ny9/92XKeKmcGqbK93VWNU1ko5eadz8233cOfX1hOuDmFNWPQL+mvtDegTctGDIfQWjMQo5HO7GslVtHHmhstivXC6uII49Y9iVewEQsmT0BEAz6yxsxCZ3fiqiol5mtPaO+yXjwU94EKoh2uLvGrThEta+EgXJsfT2iZi48P4Og3OaG0u61qCdFA8sq92qOHFY8pVez9bHilgucfncdzRVDfYWTe7nZWjjMzoMAmrRErKvB+eyhWArQjEhUFbt/k5/lxXa1nt2/y47Aps8QqKp+XrzwQLigooKysjDlz5nzVp3LWEVc/rN61hSWLH8ZaOFhhqI4YIXvsPOVOZfEcmreVce7UpXLAR0zEee3PMfXui+P/JU5+rqpSjPmX0LihRPLp7REwpl99ixQwViwkFvKTZtaxZoIyGFw9DiZUaGnd9SeyDUHZi3dvbZQp5X8ncOl18nkKH1ezZqIpIZgsfslHwGCWlRm777wGjx5Co9PhHPcLRZDsqnwcXXovBL0F99uViEG/fFPQ2rM6laib0JjT5B4bXXovMgbdLme57X1HYDqo9ECeUu5n6hVS/0p30axUStIdISj7ewi/YCbTmoUYjUglTTvLmL9oLl7rt76kb82pEzOb8Wu+mIzLmXprU+cnlTOJlkjyx89xmsi16vAF7WDN59NskvTeOsLWfMLWfEIkWi1154v8uz+dfFHneabOTSqfj1TZ3u6qxieyUIqvv6beWpw0WHY4exEIR8iaPF8uPZb9gq0OcibMo3Ht3KTrBU8wisZuQGvISS5Eld6L1t3PIWh1GHp9F//Hf1MEp96aPXhrqsmZ+GtFEDts8HC27ajC3PsyNEalEJc+Kw8xEpYfi69/4mKquXnnY9QKRFOIgGmMFqp3bWHwsDFJVezLHvs9zxVJ67lL/ujh+XHmhA2ACeuqKL9BCo4f2hPk6b+HuLvSz+E2kfPTBfxRHY/MnPY5f/MqKkq+8kB41KhR1NfXf+bXazQa7PaTvzVptdpTOu7L5lTOc+z4G/n3h/9i87ZKnNd2+b81vDQ3ee9re4McSMYtjuKvk0ucd3SWOBvMiCE/EZ0RQatL2HV0jp5F08YFxII+dI5cYkEvbo9PYTMEncFgeztWvcjq65Q7oGsmmpi4tRo6bZt62hTFj/eGBXKumaFQLowLaHne30XOxF8n9ewzX9gPz8HtCd541kuGdVkzbV1C5rC7cW1ezLlTl8oKitAZmE/qkeXt5hc892ojN77iw24UEEWSyvw7jNAWgJ+XLOTPy0upq/sPOfnf5p5Zv+Kcy8fz1hHxpH7nXwWCICKKxi9k7Gszv5BhPzcnMz+d6twEX+/56cvmbDhHOPnzbAxEMOvhgvMzkj5vt0vlhwZt8mjZotMAILQfBrMFkz0NXySGzxuipi71UuCL/Ls/nXxR53mmzk0qn494trd7lZvOkYs9wym/pnvlXW39YXk9VPrEfA69/y7TZ849YbD8zNLFXZolWflkjbxXUnjuLEl2ZGWzt9aTUP5rM+oQLh6K+61NNFUsJLt4DhG3i/Z9a4i0HUfryMF22Sjcb28lUHswwZGjff9a+T2gq3x5384yYkEfzdvKiAW8iipCqWVus6I6Tspgh0jLzGbFixUUD+tL5uj7E7yGm6tKsV1xrSKb3lPF/sMjjQz4sbSuS5UwcAdF+VrMHyrNZ398KwrEMNiyeeSO21SBLJXTzlceCJ8ssVgMt/vkm+TtdvspHfdlc6rnOfXeOXy3z8UKzzutKXnfsD4rT/45blvU83ViNAyA1uIgfdR9WAsHcWRhcUoFwqzRs2jftwaiEawpejvSHA462tqSToDtzY04On9OdXNwZGUrlKQB2t74M4iAKBJxuxLOLdxSDx/vJzuJsX3jhhI63nxZCuDDQQB0jlza9q3B//F+ObMeaW5MKtrQ3S/YpBN4ZqyyhCcu83/bRj+Gzo/8zCP3c/U11zPt+Y3ysbZI9Kz4btq+0L+hvE9/yRnOqc5N8PWfn75MzoZzhJM/T39ECoL9fj+51sRbt9sd7vxf4nMWoxs/YCCKvrN82KvxcrjFzMeHvWSeYCXwxf7dnz6+uPM8++cmlURuvu0eyp58lKhWr6xyq1zE0tISps+cC0jB8KH33+XV1lac3YK/VysXAciv+8uqZ6QN7m5iUYtLHgSg4H83IGh1eGv2KERKdf1u4aaKp1ldHOmyVKyIobliIp6aasRwkMyR99Lw8m/RGK1KN48tS0CMkTNxHhG3SxHApirXbnAdJ63fDXhrqtGl98Jy0UBFFaEu4xzCrjqFGKlWjHL3dKkiKue8b4HdSczbJiVLWqTj0gfeiqVPf+oWv5zyevfpncve2o6EKro4e2ujXOTUKI757SAjj+71cPz1LUnHXL9jNwtWrqO+vo68vHx+dccNTFIDZZWT5KwLhFVS09MofmlpCdsqFyb0CMcn5LgXnNaRI5dDJ/PUjZcYx/uEE8QT0nJo3/sXuZy6fd9qpqxXlhLftClCRwCsxuQToNWgofX1Vfg/2k+4uZEp6wXl8RUxdFffAnQpF7bueT6hTFvQaOWsdjzoT93D46P3LyT15sCRg5KaddCXkD32bpiXPLA3wu7DEUreCPL8eGXG+PlxUim33SAQjYm8dF038azydSwDpv1s3un9AqioqHztSRYEfxq+oB2L8cwPZlVODlXI79QZPGwMK5Y/iWl4D9vJsbOpWv8Q+/ZV09HcQM5538LlasA5fm7C617dKAXMg4eNYez4G+WNmOpdW7j5hhEACEYLbfvWkDHwliT+whqaohaKX2rBGwKrAfzGTDKcvXEWzZREPjVaNHpjopvHGMkeU24p02jlMmbBYEouAGq00PHmywgmO0TDdOxfhy69F1mj70dnd9JWtYTRYyZy4MDfaDz6Cc50h0JJO5791qX3InPENMX4gSMHU3oEA8z62T3cWbKQ50ZH+EV/Q0L/74/LA9x5hXJu21sbpU9edtLx1u/YzeynXsQ6cqYsdjb7KalaUA2Gv97EYjF+97vf8eGHH2IwGJg/fz69e/eWn6+oqGDlypVoNOF9W5cAACAASURBVBomTZrETTfddMLx1ED4a8z0mXM5Wv8JBzt7YnXpvcgYcqcssBBy1eJ+twqN3kTE3ST1zkbD5F7/UGL2tHw+sYCXph6BtauqFGJRssb8nKi3lf8+/zPCzfV4zGkUv9SBNyRiNYA3qkcUw3hiMaaUBzqFsLqC3FDG+UQOviqP3b5vNePWrccTiJKWno7+6ruwFkqTm6TobEpapt28rQxLn/4Kj+KWHUtTeByb5Q0BY16hpL5tMCVkjwNXXpcQ2N++0c/Nl+qZURWgpil5mY8vDHqNyPobEkvBJ7z8ihoIq6ioqKicEqqQ3+eno7kBR49N8ojbhaA3Yxo+A0fnOieaos0sGvAxduil5Oadz9R7ZvI//Ycp1Kgd3ZIJIAlSZY2+n+aqUiyFg/HWVGO9ZERnP+9MRamxY8CPEcN+Wnb+KaVrR9TTIq9hrIWDsPTpT+3iCWgMVrmkWmFneeW16DPPk5Id3fVUKhZCOMg1o8fLGe441bu2MPXWYtliavDVg9n7xq6E8Xt6BPdkUvEoAO77w3I+PNJAps3EhHUBOgIx0s1aLr+kL3+p+YBh53dlx+/cEuPB+25LOt6CleuwjlSWfzNyJgtWLlcD4a85O3fuJBQKsXbtWt59910ee+wxli5dKj+/cOFCNm/ejMViYcyYMYwZMwaHw5FyvDMiEM7Ly2PdunVf9Wmc9XT3xEvLyiXk9xIIhQGR3BtLFIGgs2gmDa88hMZgxjn6fsWE6P1gr6JcJu1H1yGG/RT874ZOwYf5iEE/gtGM/cqxdLz5stS/0i0rLE+uQhTj8Lux2p2SMFc0SoOnhYlbbbQ3N+HIyib67R8Qqdmj8ClOH3grgd6X4934KO4wZHcTmGqqXIgYChB1N9H+7J3yOLof/ZhI23FqH58gn5ulT39clY8nKGq7qkoRQ37a96+VM+OC3oQYSrSP0meeRyAiKkQb2gIimz+OUNsuYjcmLwW/yKlJGSR3+FOo3qiccajzk4qKypmGKuT3+empCu2t2UPLzuWIIR8tO5fJzhO6FAJRuvReCFo9DJjKkicfY3ogoFCj9tbsoX3/WmK+NtwHKkGjQ2d3yhv02eN/RcvOZQn9vFlFM2neVoY2LQfEGDpjCoEqSxrNrz4tV+wZ8y9BMFiwXjoM7/u7pCo3TwuC3oigN2FwFkj9wz2SCNnFc2jcUMLOnVVcdMnlgNQX3XD0MBqjBdsV15J/4xME62uo3l7G9OkPyK+Jt+LFy8FPxKTiUUwqHsX6ildZULKQ58abugW9HzBs0DDu2/0PPqxvok9eJnPvmcR1gwqhh72b4O2gvr42udhZfe0Za0f5dbR4EyJBdK4jX+p7HjhwgKuvvhqAyy+/nPfff1/xfJ8+fXC73eh0OkRRRBCEE453RgTCKp+fZJ547spF2L9fRMebLyfdTRQETUK5je2yUQmlwa6tS9DashC0OjIG3iL58m4oQaM343nvVXSOXNr3rcHSTbVamyaVs4hBDy07l2E8pw/O0bNwbXkCwWjFOPx+CjpvFJ7XX0xpExALeEjrd0NnP4ok3qUxpSHo/ej2Ps26cbpO+X0PN216GkFnwP6D8bjf2oTv0Ot0vPkyGpM1QVHbWji4swy7XiqL3roEjclKTCDhhhPet5INk7uyumv+GebBXQGeK5YyxA/tCXJTuZ/VE7syxndV+LnpUj1H3aHkZdVmnfx7+8uqZ2jo7C3qXoakoqKioqKSjC9LaPTL5ssUu5t6z0we+/3vcF47W24Ly5kwV5GZBXD0n5KQAY1nbZu3PimtF0bM4C+rnpHVqL01e2h7/UWlA8cWSTwrZ8JcYkFfZ5tXfXJR07bjaO1ZODur7RLcPDYvRoxGyJk4T5H11Zjs+GqqsVzwP/j+7x/k3liiOOdIR2PS9xNDPiJaB0889msM9nTSr7mfgm7HGZwFUtvZSOlzvrR+F2PH35hwTRsDETLTDZjNZsxm0ITNpPUoWigtW85zowVFpdxzYyL8tPrvvLO2DADtBQUpf28ajYbe5/fGn2RzoPf5vbH17fPZvwRfIhqNBm0s9ukvPJswmdBc+L3TPmxLSwtTp06Vf548eTKTJ08GwOPxYLN1CepqtVoikQg6nfR9uuCCC5g0aRJms5kRI0aQlpZ2wvdSA+GvCck88bLHzqZl57KUxuhiODH76f8oUVjKOXqWVALdSby/NhryIeiMxICYrx1vTbUy67p1iex111QpZZqjnhbsV4yWVQfjZvQtO5P7F+vSe5Ex8BYyBkr9wYEjB2ksn4/dpGP1ODHBoqn4JT8d/9gIkRCxkJ/cyfOJuF20Vq9MUDm0FA4m8laFpKAY9JE18l5clY/TuOn3aAQwR9rxhqRz6S6WVfJGUCH9H1c3HL/WhycEFzk13HSpnlXvhdEAU9b7e1gvBRgw6oakmxdLlz4JoAbDKioqKiqnjc8j5vdl8mWK3f1P/2GMGvYm2zaWIEaj5Ez6TUJmtmXnMjKHT0MMd/kCxwWitNYMWXzUmFdIXd1/SMvKJVhfQ/t+aW2j7Ot9gKYN82lc/xCCziDrmCRv3TIRdTdLSYtOF4v4Zr6gMyDGRIiFFJnreBub8dw+eN5/rbO0uuv5rKKZNG1ckEJENZ/M4dNo3FBC+jX3J70O8Tayurr/4Ha7U9q55fUykaYJ09ymw2LMJdjNzm39K5UcqT/O8FXSWmnu1UamXKpnQIGWD+pdhPKchNES0uSk/L2ZzWZm/+Jn/GLuIhjepdbt3VnGb0pm4zrBsV8lZrMZv//0ZoR7ndbRzhwyMzMpLy9P+pzNZsPr9co/x2IxOQj+4IMPqK6uZteuXVgsFmbPnk1VVRVFRUUp30sNhL8mpPLECzfX47z2gcTdxMqFCAZzYvazJbmwVLSjSf45LroAIIZ85M98ibo/3JRQ3uMcPUuehDV6M553qxCMFgznXIhgtMqeesa8wqTm7U0VC8kYcmfCuYghP+4wKSyWRLT2NGJBH7FwUL5xye8X8qHPzMdSOBjPwe2I4QCCVo8Y9KG1ZoDWgCBGyDaEuesqAxs/iHCkPabI6iaT/pfUDUNY9NLzfBDhseGShdKq90IUv+TDGwK7ScvV19zAtJ/NY+qtxQmbF4ycobAgUFFRUVFRUflimD5zLhddcjmLSx5MuvYJN9fh3l5G0eiJVL9RLfsCd9chAWldlHPet/D7PLi2LiHqdiWvcgv50dszIeijqWIhtstGJbRuNVUuwn5VMf6P9tO+bzXCx9VyC1jwuz8keOxDcnps7ANY+vRHDPkJNX2iyBR3fz4W8CoUprt/jniSI/l1qFd8zngQ/N085TpM0OsTrnHcq3zDKxUsnPcbKm60KKrnAHrZBPr0ziGMlkbtudR8lLqc1WCM0avvOO76qcCaZ5+irv4w2Xnnc9dP59CrbzF//Sj17/urxGCMEQqeuEz3ZJn4P6d1uLOCK6+8kt27dzN69GjeffddLrzwQvk5u92OyWTCaDSi1WrJzMyko6PjhOOpgfDXhJ69LtClmpzMGN2gAf1VYxOCz2TBcbC+Bp0jFzEaoW3fGtxvV0o9wgYzgl5SJxRD/pQ3kbbXX0Sfcz6xgJtYwEvLjmWIoojt4iF4D1UTrK/p8i/euYywq66z90bqp+lOsL4GjclKms2SwmIpB+Pw+2neVkbU10YsHCDtf67vLIOWSqvDLXXwMWQOnYq1cJCUZV7/sKSordFgN+m569Iwq/8Z5tliM/UdMW542YfDJHC4TSTtBD3BLl+MXKvAIVeMkjckS6a8NA29HRrKikxM3Z0ui2Sl2ryoO/rJqX4NVFRUVFRUVE6CwcPG8OcXlidf+5hssgjU3jd20bj+EWkDvZsOSeDIQdw7ypg+7X6eWPBLMkffT+uuP50w8xrYWUZ7qwv325sRgz6aNi4gFvSiMVqxXTGGjIG3EP7vh5gOxoU6pRawKev/RqDv9UkztlprRlLBT8XzehOIosLfOH3grfJaKJU7iD4zj8CRgzRVLMRqMrBp5UL27/8rx4/8h169v81PfnY/o8ZOAJTK9r5gVzVd6e+f4LkiFJV8zxabubvST0xr5Je/mSoFwbU6PG5/Sls3u06H2+9n+MARDB84Qvnkac64nk7i563y+RgxYgT79u3jxhtvRBRFFixYQGVlJT6fTy6hvummm9Dr9RQUFDBhwoQTjqcGwl8Tkpm6N1UuwtZ3JGI0gtaagVaMMHPuYwweNobiYX3J7j8Fg7Ogq3f2/7N35uFRlWf//5zZ16yTBNlRkZIqKr4tLz92BZQtEKTFoFhfwQoqVUqlSsCVoA3SoLSCVaxbiYAQSNhkX0tbq1jUoNKKEEQymSyTyezL+f1xMic5ZEJFQEDP57q8Lsk5c+aZOXM957mf+76/37T2iEF/C7N017oFRAMNsgiVGPKjT++A+apGA/b1C9Da0lop77Ggz+xC8MRnZIyZpRhbw4cbQWuQe28s3fqgtaZS+XY+bScvxvfZvhaBurOkAEGro87VusWSsX02EXcliCKGy7opep7r9hXTcHAz5q69cf91Oa51zyEYLIiRsCSWFQlQ7w6w5lOBpTlS+XPxR2Es+iaf4EQ9wXmr/Di9UpA8uaeexwcYZT/hQFhk4S3SuV8e/1r+flrbvDidBYGKioqKigqoQn5nS3OBUXuqg+DGIlKGNWVKPZsX8eCvpY1rqY3pIVLia5G1z9JwcDP1+1cg6I2AyIKCR9CZbYRrviJtyFSFLaV7XzGRupNozHb8G5/F467HZhDwxrQIZjsao5VY0IcYjRDzezj+4l2YQzUUj1daMxbfambshp3QmImGpqRDVdn8hIKf8eOuDUWk3/IA1uwBeMt3UbvrNdIGT8HYPlsKctf+DkFroPLtfHQpbUjukye5jJTNJ+qto3rTIlIH3U245ivWlpbhGPUwHcZJ38dz8wpINmoZnTu21e/78Jdf0XdCy0q+L2pFXnlhOsNv/z+O1Jhp8NSe1ttc5YeNRqPhqaeeUvztiiuukP8/Ly+PvLy8b3w99af2PSFeShtX8UtKz0IbCeB5v1SaqA0mTI0lKzu3rUdjNFO3rxj/4f2ygJS5a28i/3S2MEtPHXQ3YixK7Y5XW4hF2HuOxPvxNmLBhhblNq4NRYiRMMGKjxWK0PH+5ao188gYMwtnSYEUbHtrETRa7EYNFQvGkJyeifmKnzQG6hWgMaAxmuUxuPctI2f5O3hDMZLTM9H1m4g1exCBowfRJWcBEDr5OZm5s2UFR//h/cS8dXg+KMPecxSX9XlBDrAzcmZSs3UJlkg9h1x1cvnzqT7B8Z7gW1f4cAchyQj3/8QgB7+TSv38OENL3jV6JvbQ88f3QtxR4mfODgFHapOEe6LNi/9mQaCioqKioqJydiTS6AiUFRLYughno39wXAk5URuT/bphNBzcrBCjqiqbj76ttPlu6zGU5L63U7nyCTRGCxk5M4l6qtDt/SPLRuvo29HetIkeEUi/RZnEEAQN3lBi1wl3tZPmZjCS64WZWCiIoDfKG+xx1epwdYW00e9vQGtNbUqORMM0bPo9lXXVaCzJCDoDjhFNa7iq0kJsJgNWox7zqCbnkRNL78NxStaZwdP4/XO/P20g3LVzO57c5WTNpxEOuWJ0d2gY8yMdP+qSxc/G3IS31VeqqJw/1ED4e8TAm0bIAfHi5wvYtKkUMeyTs7feT7az4NnHEHQ6xFishTq0s6QAkPoXov56tEkZhF0V1GxZLKkTJhCScK17DnOoBm8YLJowzpWPS5NsciYp/SZSvb6IWDTciiK0F1OnHmTm5lOzdQmpg+5Cu6OomcdwAxNKt6HpnkNDyE8s4FWoXMsWSyUFGAc/JO9qujYUSYqMl11FxH0SYysKjs2VEMVQU69yzaY/YDc0lT+frie4u0MqeT611GfaxgAAyz4Ks/rnlmZCWfXk3HgNme27cMcv7mXq1If4y5svU9GoGv1NLAhUVFRUVFRUvj2JBEZTR82Eva/w1raDinMTtTHFhUWj3lq+fu1XhKuPo0vOwn/kAHpHBzzvlyGG/Ah6k7xucS+9mxWjdS0yvLmlhhaJgsq380lxZCZsAbMZtQSOHlSsZdJvvl+qqFv5JM6Sp7EbNHg8XpJSUtAPfwitPYOqskLq1xcSaKgns11npj30KKPG3EbZmrdZuOApHKe4iGTkSN+H86svSWn2+VtTuq5Y8Z/Tfuc/7T+Ype+8rqimm7DaT87YXmdy61RUzilqIHyJ07y0J26/A/Du1k1knGIDYP3xjXg+WIfWnEQs5EejN1G5fDb69PaYOvZAY7TgGD5dthGwNhq+O4Y9SOXy2S0mvqinCodYS3Ez4YMJpTHcGTcQOPYR1RuKEIwWxEgksQeeyQo0CTFE9r/JirHKoHJZToSct1cSjRogGmpV9t+18XmibieC3ojGZMPecyQNB9ajT+vQqoJjcyXE+HnW7AGEd7/EAz2istpzd4em1Z7gREFy345auUd4wjV6pm0MyLufk6/X8cdDKdB3MosXL2Tq1Id4e9W2S0LNU0VF5dImhJbmUjZfuwIXbCwqKhcS51dfkuZxcWLpfXJVXFKvcdQ0anTs3LaelxcvwOPxIIoix1/8BbFwCEEAMRRAMJio27+CaN3Xyvat1QVEar5CDAfQ2tKJNlTL6xZ3dVVCkc96t5sUaJbBPY5gMCF2HciE0lKW5UQUrhMNUSMNq55CDAcRDCZsV98kbehHIxANkmkSKM41N2adQ0woXUyk3/1kjJqJc/Vcfj3rGcWG+6GPPyQaSCySFfcJbr6Ga03pul2nKzgd/9i9lWVjlaXey8aauX/P37/VPVRROReogfAlTGv2O3oNLcpW0oc9KIlUBX1Egj4EgwUMkrmbGAnT8MkO7D1HYurUQyp7aVR8jitBJ5r4wvv+zIpbzS0C19Er3iMz92lFmU/cO6/536zZAwFpAtXa0nC7nAqbIogrQUPW+MdxlhS0IuLQgbQhU2Tlw/gDoX7/CmzXj6CqbD4xX91plRDNV/WWe6Pr6z08PsDGvD0hcpf7qA/C7av9/OWUXcxJ1+vh00irQXJ5VQxfWBLcaq6Q6K5z0rGZQnQiLz4VFRWVc4kvaMdi9OC1dqYuEKX8c+G0gjQqKt9n7KkO6va82aKdy57qYOe29Sxa+AxRrZ7MW+co9EWaV9FVlc3H2LZbU1bYVYHGmiKfc+KVqehS2sjrluT0jIQZ3qTk5BZVa3X7ivEc3Aw9chi7YSfuaidWgwZvzCCPKT5m76E9GNt1R2tNxWaUtFNOXZeN3fAmlrv+hBjys3jxQg59/CHvv/93Kr86gqA3K8YZJ3i8HK3Jyg039GLn5kVyG5e5a29cZfOx9hgqCZHWVKAxWsi7bfxpv/PWeoQ/+7LyHN5ZFZUzQ3OhB6Dy7Wle2iNodVLfytBpuF0nEwd9NRUIBhNo9WhMFtJvmUbHGauxdO8PQP3+FdLuqKuihdF73N4ocPQgYjRC4OhB6uvqEmZDG4JRxZgyRj0MsSjO1XM59lyu7HOXeuNkAkcPUvnOk4ixKEkpKew9FlVcT1KCzpA+W8+RVJXNV4yhqmw+4eoKqjctkoNgiFs8mWn4cBPWbn3QGCUlxOY0V0Js+Ne7WLr2ljLEepG9x6JkZ2ioD0J2hoaTDSK3rvBhKvAwbWOAygaRP74Xorwqxu2r/ew4EiEcFdlxJMLtq/2UV8WwG5EFt/RaQS6btps0nFh6H5XLZ+Os/Jotm9ac09+FioqKSiJ8QTtHasz/VZVVReX7jqDR4hg+XbFWcQyfjqDR8tbrLxHTmxXH/Yf3y2rMzdc2gWMfUbf7DdIGT0Gf3kFxTsRdSXKfPHntpOt1OxPWRhTrhbzVfup9Qer2vCVXrQlaHan9J2LrMRTPgQ3UVVeBwYIXi9yi1nzMGqOFuj1vSUFxUGylr7hKtr7UdevPu1s3Qd/JdJxRghj2K8Ypr69KC7FcN5yde3YysN9A2PsKFQvGYqx4j+t7XI33o82kDZlCxxklZIzJp3jFan5y7bWsLUns/9q1c7uEa7wko8DKNdvO161WUTkt6mPwEiZR30rE40IwJrZAktWRNRp5gveW78JXvrNFtrZuX7EiC3yqBZPe0QGrIbGNUHK60szc2D4bMRIia/xcaWK96v8RPP4xxxaMRTBYELTaxl6baiaU/lFRBhRXggZI6ZNH/f4VUhl0fRWCzkDa0PsIff053s/2yiIQTUJeo/B+tJXUGycjGK0t1LCrygqJed04Vz+NGI5g/VFf0m++D2/5DvJW/57J1+v5yhNS9AAXfxRmzg6pnDAak4Sy7r5eLwtndXdoWDBU8g/WPlWf8IHkCcTIHDNFHkfRwmeZGgiovcEqKirnjUqvZPz5tSugBsEqP3jcrpP4momCJvcej6VbH5zVldQDIigSCq31xYpBH+m5+Zg69SBcozxHn94end1BSv875fYtrSWJMWtFPPX12IxaIh16ofn630TqWiYw9GntEKNRQEBrTk54TnOXDG1SRqvrsqTkZKpKCxGDPjwfrCMzN1/OZAs6ozzOuIuILjkLURRJ7T+RQKdr2bz2GR789WwG3jSCmgg8cs8YHCOVlYcZOTOp3rSIOY9Lir6nCmdNmTGLCb99gGWyDoxUJXf/T/Q8V/Qqw2+f9G1vp4rKt+a0GeFAIMDevXvZtm0b1dXVLY4Hg0Heeeed8zY4FSU7t61n8p055NzUQ1IxTHW0yHK69xVj7zmq5c5eWSHGy65C0OkRw8GmnpVmvbPNdzk9H6yTyl+aXUdrTSUW8pM+YjptJ72IVzQm2N0MIHYdKI/HW74D9yt3IYgxglsXYr92KN5PthNxOxF0BohFZA9ia/YgIv3uZ+wGG8aCBnLe9hHpdz/W7EFAUwl1u3teImv8XBA06OwO0oZMIeZ1U7NlCccWjKVm6xJS+t9JSp88og01HHsuF88H69A7OlG9aZGUlS6Zi63HzXT8TQmZY+egsVhxlhQQOHoQS7d+uP0ib38cxh2AvFVSxvfNf4V4ZFuAl0eZCc6286teBgDm7QnhD0OmVWDRMBPjsnXsOBKRBbeas/eYlPlWZPGHSCXSKmeGOj+p/FCoiZzd6+NB8OEjtRj8ahB8vlHnpoubndvWo7WmNGYzV5M2eAp1u9+gbl8xme06k9muM7rkLMX6Kp4YaI6UYDDJgpwao4VjC3I5sfQ+arYsIeZvoPLtfOr2vEVK39sxd+1FLBqmvt6DxurAp0vBf/jvoNWitaYqru8t30XdnjfJvHUOHWesJv2WaWgsydTtK1acc+KVqSCKaKzJOIY/hGnI9JbrslV+6j1eYj43+vQOUoucxyVnstOG3odrQxFaayqX3fUCWePnIsYiaHSSooCxfTaRQAOLFy9k57b1AJw89kWrQbmtUUH6VHJyb6XSG2PaxoBcXVdwo4nHBxj57Kjz7G+sisq3oNXH4X/+8x/uueceamtrAYhEItx777088MAD8jkej4c5c+Ywbty48z/SHziJ+oGDG4sIlBWSOqrJ0ihSd5KUBP7AMa+bqLkaMeRDY02RM72n2+X0HdpN1O3EuXouYsiPxmRFFLTo7A4AbNcMofKT7eSWGqh312EzStlOzcHNmDpdJ1sFrJCtAhqYUFqKOxxAm5SBY/h0SayrUahKyjwPguxBGI8exFkyF6s1Xc7yutYvAEHg2IKxaG1poNHKlk36dKlPuHkWPHD0oBQ4T1kqZ4lT+t9J9eYXyWzcwY2LU8R8bgS9mcqVT0I0hAAcut+GXitQ/FGYaRsDHHXHKL3NInsLL/soTMn4JqGw297xcfdaP0fdIklmHZdf87/ctf5fvDaimdDFKj/6gfe2+L4rGgU6VL4Z6vyk8kPjyvZSb53FeObCel2McKTGfK6HpJIAdW66OFn8fAGbN68j4m9AMJjJHDu7hY5K1ZoC7pnxGACLFj6jsIQ0d+3doqrMtaEI0FC19lnCziNkjJmlqKyz9RhKSp882YpI0OowdeyB/8gBxFhEYUdZtfZ3uNYtwDFyhtQjvOctuXIvPsaMnJk4S+Zi7nStLGrqGD6dmi1LFOsfr0ZDbulS6uvqsNutNMTsZP7sEcXY4naY8dcIGq2i4i+l30SqNywEGtvI0jtgHzyFt15/icIBI0jOSNxTrE9vf1oF6au6tGdR3xpFxnrHkQjdOmUmPF9F5XzTakZ47ty59OrVi3/84x/885//ZNasWSxdupQZM2YQi8W+yzGqkLgfOGXYdMxms9y3wd5XSHa0kdWP2056kU4zS0kbMgXBaCHsqkBrTUeMRXGtX0Dg6EH0aYl3ObW2NFL63YEuOZPMsbPp+JsSMsbMgkgYZ0kBRwtHST0vqW2p94UQRYEG0YSx03UggnPVUwS2FLGs0Sog3iO7LEeDzSjIE7w+vT3mq3onzGCLQT/OkgK5rzgWCeMY8Ws6zliNoNWTOeZRUgfcJfsMt+wfLiTaUMPxP9yB7/O/kj7sQao3LYJYlMrlszm+ZBI1W/9E2uApOIY/RJLFgBANkmQUAch8zoPmyXoK9gTJ72fEG0LhLXxq/+/b4yxoNZDsyMQ0+ik+++Io1/QbzeQdKZgKGpi8I4WA0YHWntHi+85s1/k7+R19X1DnJ5UfGoK+Se/ZQPSM/lP57lDnpouPxc8X8O7WTaSPfpSOv5F6YhNaOgZ9sg3l4MHDEP0NOFc9xbHncgke3ETMVy+vSarWzMNyZS8yx+YTOPqvhJV1/sP7m/6dMxMxHMBy1f9DEDRyECofH/1bxFhUvn5rZdBi0I9z9dPUbH5RXkedWpJtzR5E8uTXEAUBvzGdjNGPtBibGA6c8poBtJ28GASBtpNeRGd3IOiNOFfPpXrj8yT3Ho+xfTbOr77kr9vX4/P5cG0oatFTHAv6qNtXTLtOV/D4rEfoeWV7rmybRc8r2/P4rEeYMmMWd29AkbGetCHGb6bffX5/BCoqrdBqRvjgZOy1ygAAIABJREFUwYM8/vjj6Bsfvnl5eVx11VX88pe/5OGHH+a55577zgapkrgf2Ng+G2d1pcLzbue29Sxc+CyOZruWUr/sSDwfrANE7NcNw/vxNpyrnkYMB1rtna3Zshjb9SPkzGn1liWgATHkQ58meRM3HHwXMeRHl9IG8+U34P/Pe/IuZ8WCMfTtqKX4ozAFe4KyhZA3KJLWTISrbvcbWLIHUrNlSaOglxlTxx5kjH5EzgSbr+iF7/O/4my0DIh/fkGrk/uXGz7eLh8XjGbsPUc17caWzSdUfVz29Gu+o+v7fB/WL7axIkdD3452ntwV5I/vhagPQrIJuqYJPLItoOi9Ka+KKWyR8vsZGZet44takfRRE6Vd0qHT+OfeV3jljc2K+7N48UJZfTF4vBzPlkVMnfLQef4Ffb9Q5yeVHyJd0vxnHdjWRFBLo88j6tx08bF58zocox9tZv/TIWE2M6tdF0B6Tu/cs5OMZurMtWWFaMx2xfqheuPziLEYYiiQMGiNu1LE/x0Leqnb82aLIFQ+7nOTPmI61RsWykmKFlovRjOZubMVlpat2Rlp7RmtV/2Fg624cEgCoq4NRaDVEzj2EcbLrsKaPYDA0YNktuvM6rdeInXkTKLeWrn3WZfShtRBd6OzO3CVzadNhyy2vPM6JeNM9O1okiri3nkdgAeffI6pC+Zx+Muv6NalLbMfu5OfjbkJ75nfWhWVs6bVjHBycjJff/214m833HADL774Ilu3bmXWrFnq7uZ5pnlPsNaUWPW4eSYx7ikc9dZRtWYex57LVfTLikEf0YYayRt4xK/p8GAxCGDp2lvahVyQS82WJdh63Iw2ORPb9SPwlu+kZssSarb+CUGrIzN3Nh1nlJA2ZAq+8p3YetyMPr0D6bdMo+GTHYpd0eT0TJ7cFSR/e4BFw0wE8u0sGmYi0yrg3rcMkHYhU/rfie/QbsLVFQh6E/aeo8gcO7tJFXHEDLyfbCcW9IMgACAYTC2+D53dARotgtFCZu5sUvtPVOyAhr7+vMUurGP4dCh/l2U5GgZ10fFOeYRlH4VZ/XMLgXw7q39uYeeXUaIx8IZg7AofuW97aWMTFJ8pf3uAJ3cFSUpOknua47unzRl40wimTn1IkcWf/tAjqlDWGaLOTyoqZ0aWVUfXLqkXehjfe9S56eIj4m9QBIPJvccr9E8CRw/i2byIO34htS299fpL6Lr1p2brEo4tkLK/oYC3xfohfdiDeA/tkq2HmhMvE1b+u4OkTG20tNJvbKZ6QxEaowVdWtuElXL2nqMUlpbxz3Pqua4NRcTCAbS2tFZ7m6tKCwkcPYj3423UvXQHzrdnYfQcw7X+OVIH3EXm6EfQmpMInTxM4OhBassKCQT8VB0/QtWaebjWPYcY9JJ1WwHt7n0Z29U3SuuqUQ/z9ZHPKR5rUlTNFY81sWbZn1myYB5TZsziq6rD7P3Hdn425qZzer9VVM6EVveFR48ezaOPPsr999/PTTfdRFpaGgC9evXihRde4Fe/+hWHDx/+zgb6Q+PUnuC6fcW4yuYrMr2ezYukoOqU8zs22600deyBe/9yXOueQzCaESNR2RsYpJ3RwJcH5J7ZOOZO18o+wlVr5qEx2Um/ZVpCb+JwzXFMnXoghpSG7LreE/nDlt9T8nOlp13xrWZGr1hFoNN1GNtnSyJcYT9JvX9O/d9WktInT/FdxHcvNZZkMnJmEvG4qNn+iqJ/J57JtnYfQMOHGxPvgIYSl0M5AxH6dpT652ZuCWDUweA3fXR3aBjUWUuSSeC10WZFn+/knnrFZ1qaYyZ3hR/9kGnytVsreY6XXsWx2+14PGfe9/dDRp2fVH4onK1Qlsp3izo3XXzozDZF9tOaPYCQ6xjO1XMhHCCzXWemTn1Ifi5XHj+CUF2N1mgBBDQmO2JMJOJxKa4b11NJHnwv1Ruflz2Am/cIyxonG4pAFKlcPlvSN2nWDxw/336DsopNa0+nas08YgEvgsGMGA4T83tkm8uqskIyRs3E0q2P9HlKCqSKvfQOpA64C601FWdJAc61vyNz9G+b/Ik/KEMMB9CY7DhXP02mPsCKW82NWi5RJpT6iBAj4nER9dYhhgO41hQgCFrErv3RhnfiaPysxxbkJl5XBWMJXTN8YVjct4a7H/8NRt1cxo9TkwAqF5ZWA+EHHngAvV7Pyy+/TMeOHenVq5d8bMCAAbzxxhvMmjXrOxnkD5HmPcEAqf0lC6Hqtc8QDXhbTNynnm/q1ANL9sCWBvClhUQ8LlkkKlxdgaAznvI3SWArXC35CccCXmJBX+LSn5oK9OkdABSiVyD1qRxbt6BVr+HAKWJeqf0n4j+8vxXrJzOCRkvl8tlojBbs149QCILpkrNAhMCXB9A0Zs9l4YjyXdTteQtBb0x4bZtJCnBPNoiEY/DGqKag9/bVfu6+Xt8ikJ+2McDcG5WfqT4gktlM3Kv5RoXKuUWdn1R+SMSFslQuftS56eJj6NCRvHtKIsF7cDPDho1m6oP5Lc7X6E0IegPptzS1MLk2FFG741UEjVZeJ+mSsxAMpoTWQzF/A54Pyqj/2wq0SZmI4RAZjcFo8Hg5ztUFknVjKIBgtGDvOVJe55k69cDWYygNB99VCHC51i/Ae2iXbHdZt69YDn4FnYm0oVOxXd20MBGjEUkg1ZxM5conIBpGY00mM3e2fE3fmjkU36pMVizLiZC79mUaRCOZzcrDXRuK8H68DceIXycsM/eW7yCy/03c1U7sxsQ2Tt0dUvXdq8Mj3Fe4gNxxOYSsnTF4K6gLRPnaFTiPvwSVc40QC6MLnrzQwzgrWg2EH330UebMmcN9992X8Pi1115Lfn4+kyapvl/ng0Q9wSl98vD8bSWlzXqCT3e+//MmA3hoUh2sKi1EozdiyR4IIoRrKqjeuAhBb1D0/0YPeqjbV4xgMKO1JLfqTZzcezwAurS2OEskhen4NWwGTWJPu6QkfI3/FqNhtLZ0oKnEp/nOqtSrolPsnro2Po+h/52SjVP5Ltx/XU7Md1La5bSkyH3PzZUVIx5Xiyyya/0CRMzkrfJiM9DigfCXsYmD3kMuZWnb3mNRbEatLNylM9lkzz2Vc486P6n8kGgulKVycaPOTRcf8WB389pniPgb0Jlt3Dx0ZMIgGEBEJOMUxWbH8Ok4Vz1F7a7XFGsIZ0kBVaWFZOTM5LK7XpCr8ZJ6jaX+vTVo7Q4EQYNj9G8V18scm4+z5Bkp0xv0taiEk9ZvM5VjGDED17oFigRJvHoPkB094sTLsdMGT8G56inQmVtc0xmMJkxW1LvrybxtXoLv4OmEZea27AGYD5UqtFYmrPazbKxZ4RlccKNJfo/P3zqBL2jHYvTg1Lal/JhO9Ti/1NCbiF52xYUexVnR6s/t3//+NyNHjmTevHn06dNHcayhoYFnn32WVatWMWDAgPM+yB8ime06Jww8W1MXTnR+uKaiFZGEAJZrb8ZXvpP0YQ8S8bio3flnRea4euPz2HoMxfPBOsSQn6hWn7AU2dTxGizd+lC7+02CJz5T7DRWlRUSC8bIWx2guJmBet4qP56YjYxhU5Q7neW7ZOGr5jL+sVAArckmWS01Gt87hj0oT/61W1/CZtRQL0CS1US9340YDEhlT2KMzFsfS2gRIBhMIGhI6jmC+g/LcNb6Ez4QEgW9doOketj8M3k1dtJ6j8fSrQ8VC8aqQfB5RJ2fVH4I1ETAZpfaNrqk+S/waFS+CercdHEy9cH8hIFvXFvF+dWXZLbrzB2/uBcxHGxVYMrRbD1h6tSDzNx8Klc+LreJ6dPbk9L/Tizd+lC/fwWIYkIF6IjHBWIMrSWZqCh+4/WbpPPStFaKi3I5Rs5osUaLW0Ya22cjRoIg0uKayemZ7D3W0CJZYTVqEpeChwMJy8xjH6xk2XiTfJ0fZ2gRxRA5b/vwhqCtXWD+EBN51+jl9+jauR0gWbt97QqoQbDKBaFVsayVK1eSm5vLvffeyxNPPIHfLz2Et2/fzvDhw9m+fTuFhYUsWbLkOxvsD4k7fnEvns2LWhVz+CbnC3pzQpEEMRzE//l+Wdiq/u/vyJnj5iIQ/s/3IwZ9kopz++5EPTV4V+dT8dwYfGsew5jRmUjtCY4tGEvDgfUtrpExaib69I4ErhlHzvIAxgIPYzfYqIpYyGhUcGwuiFW9+UWOFuZQt+ctokEv9p4jiAV9CBot6bdMUxjfRzwuwtUV1O5YSoYhQElOiGC+jZKcEFmmKFpbKpljZ7d4oDVZBICgN2HqeA2mj97h5o4huZSnOXuPRUkyKKX+80qC1Ed0jFkRxDjXQ26pgejAh8gY/Qh1u9+gbl+xaod0nlHnJ5XvO/He4MscJjkIVq2QLn7UuenSQXZx6DuZDjNWQ9/JvLCoMKEYZ/B4OYLemDA4JRrBfFVv9OntCVcfx71/OVVrn0VjTcEx4tdy+XBzane8isZkIf2WaaQNndrCikgwJBbU0qW0wb1/ueJv+rT2ktZKwIdz9dOS8GmjUKo1ewDB4+VojNaEol5i14HkrfIr1jgTSmNoe47Dva+4xftrbWkthMZih3fjDTX1BBd/FCZ/e4DiWy3UzLSz7U4LURE+qYrK73H3RpgyYxaV3ogiCG4uEjv5zhx2blv/7W6uiso3pNW9F61Wy0MPPcSgQYPIz88nJyeH7t27s2XLFnJycnj00UdJSUn5Lsf6g6J5729F405l857gROcf+vhDRemPGPLj2vi8LGogZV6LEAwmxW5ja/L64eoKtLZ0HCN/jbNkLlmWGMW3NpW5TCj9lEi/+7FmD+Jo4ahWr+H+m9QzI0ZqqHNVycdOPVcM+eg4o0TuZW4o34XWZFP0o8SD9OpNixD0JsyhGpbdZlH2t4yGnLdriHprEYwWji3IRZ/egeTe4xUPhYxRDxPY+CyTr9ex9ECYaT81MKnUz9IcZSnPwM5axiz34QmCzShA95sRP3yXlE6XY/7pJEUvMoKG+v0rEBxt5An81N1mNVN89qjzk8r3mXgQ3LVLqhoEX2Koc9OlQyJtlbThM6gqLcS1vgjHiGYVcKWFiLFoQv0RAM8HZdh7juKyu/Lkijhj2x/J7VKn2lSK4YCsQg3KajV9egdMHa+RS66bZ3iT+95O9YYiWYukqmw+sUADlW/no7WmorGnEfVUY+7aG/dfG4VSDRbEoI/Um37ZovXM86/NuH0iYzfYcFdXkZyega7fRJK79cO9fyWBowflc+s2FmExaPHUOHGtKSAa8CHoTaSlJpGelcreY0EGddFRsCfI0pxT+o7HmhmzIsi8PR5+dEU7Zs6dwXU3jgZQBMHNRWKDx8uljQpQ100q543/WoRw7bXXcs8995Cfn09FRQW9evVizpw52GyqeMf55lR14dMR971LH/1okzLg+2VYswfKE7HGaCUW8ILWgGAwyxN6ax50upQ2iLEIUW8tSWYDxTm0EFQYu+FNAtZ0efcy0TXaTl7c9CCJ6NBakhL716V3UJjPV29aRMRdmTBojtRJzfleSFjO7A2Bb/srsqhEvK845DqGr3wnsUAD/o3P4nHXs+ZTDcvGSpP2jzO0sj9wshHu+4mBpQfC6ATYdqeFu9b6qf9sC1q9kYovj9BhrDQ2b/ku6na/oXjA/OEPzyFGwqSOmqlO6ucJdX5S+b7S3OpIDYIvPdS56eInkbaKsX02MX89jpEz5LWToDdjv2EU3o+2yeXHzfVHmgeqBkdHrNkDsPW4WSFWKglbzUUM+qW1VYJqNUu3Phx7LpdwTQVRbw2iKMjroHjJtdaaiqA3c2xBLlp7BmIsStbPnlSoT2vt6S2FUsvmE675qoWol6DVoXd0xDh4Ch2brcmkqkIj1WufIRJoIKtdFx544Df0GDCCLz7eyYJ5BVjHNK2vKtf/jl+URXh9FBxyKRWjiz8KU7AniCcQI9miY/rM6Ywal8ORGhDDYfm8RBsTDJ3GW6+/pK6ZVM4brZZGA5w8eZJf/vKXzJo1iwkTJvDnP/+ZkydPMnLkSHbt2vVdjVHlG9B8AvF9to+GA+sRQ34aDm7G3LU32qRMMsbMouNvSsj62eOIkQhVZZKHXFKvcS3Kcqo3Pk9KvztwDJ+Oe/9y6t3uhAGn2+WketMibD8elNDHLqXfHYrgVmtJIqXfHS3P3fi8LLoFjcGuuxJtUkarJUoakxW73ZqwnNlm0rUo1XYMe5CGA+sxX/E/ZNk0rBkdITtDo5i0867R8/F9NgL5duoC8PzfQ0y6Xk8oBtM2BjheL4IYxaKL0KFzF3ls7v3LFR7Kpk49SBk2nZjerPibvXFSVzl71PlJ5ftOllVtmLsUUeemS4O4tkpz4v6/1uwBtJ30Ih1nlCCG/aT2n0jUW0NKv4nUbF1CzebFOBoFtZq3lMXLlpuLlQpaHan9J5KZOxvBIPX8x10sTn1vwWAGjY5YwEva4HtAjJE1fi6X3fUCWmtqY0IhjKA3EfPXkzn6kVNa0h4mWncyQavaw3g+WIfWmspld71A1vi5iLEIqQP/D1PHHlSVzT/Fs3g+1h/fSProR7GktlFUs/3phYVYB09TXF9o0506b5ib3vBhM8CTu4JAU5n0omEmgrPtrB5nYN7sxyh5p7TF/XB+9WViK6avvjwn91tFJRGtPmWLi4tZsGABbdq0YdmyZVx77bUArF27lvnz5zNlyhRycnKYNWsWycnJ39mAv8/s3Laev7z5MpUVX5xxGa3zqy9J87g4vmQSUbcTXUobUm/6Jb7P/4rn/TIyx84m6q3l69d+Je1wGixS2c6WJYRrKkBrwLnqKcRISN55tGYPQIxGCFcfJzk5mb3HQi0FFQyQNnkxvs/2oPnPbpxvz8JqAG9UT9ot02RBB2gKbuN/c655BmIR2T4g+NUh+VjweLlUTh0NK3Zg3fuKZaGrWMBLg2Anr8RHcS7NSrZjeAJRUhPt9Aa9aI+9Jxu95/czMnW9P6Gy9eWpAsEIrPk0gjcEgYjIa6NNtE/SkLfKzxUds3hv6yIYPK3V8vKIu7LF3yrUSf2sUecnFRWVixF1brp0uOMX90pVWkOVNkmpA+6Sz5F6cBstItPbo7M7aDtJ0jNJ3A52HDiNWGnIT0yjQQwHcZbMxd6zyTvYtaEI+w2jpESGRpvYlinoR6/XEQ4GQBASvkcs4E383kEfrnULiDbUoLWmIsaiaK2pBI4exNZjqPw++vT22HoMxX94P+k33wdDp/HKSwvlNi90RtKaCWnVbFlM0tf/oPi2Zq1zq/184oyy62iUVT9Xtq+9OkyyTnr1xvGKMZ6pSKyKyrmg1UC4oKCAe+65h/vuuw99M+sGk8nEnDlzGDJkCPn5+YwYMYK9e/d+J4P9PnO2vRH2VAd1u1/HMWJGiwnd/++/S0Hk3r8oSnfjioLW7AEEjh7EufppssbPTejhW+8NkrcqoOgRzlvlpyEE/hd/QYbOx4oxetmQPW+Vn0DNccUY48Gtt3wX1Vv/hKDRkjHmUUXpjhiLYf1RX1wbioiFg4gBD+nDp+Na/3vEaETRLxPvwXGd/Jyct2vwhiDZkYmu30T0+1cmtnvSm3G7nPTtaAekDPCDm/zcucbPG2OaPttda/3U+UVSTAKLhplayP8X32om5+2/IphTiO5chKBL7FGsS85q8R2ok/rZo85PKioqFyPq3HTpcKq2itZkQRC0UpAo9+AWYutxM6C0d9SnJW4p06e1V4hdNT9et68YjTWlRcly/f4V6B0dSB1wl6Q2/bcV0rqnMQlw2V0vSO1uH5RBNERUZ0FjSUbTrMWt+RiEBH+PW2FGG2oQjBZi0TDpQ6ZIyZDqCi7r84LsYwySB3H931YCUhBd6TqJN6UNacMfQmd34NpQhKDRSsmL8ncp/rmpRU9w7nIfnlDi9rXP3zrR4n4k2pjwbF7E1KkPnc1tVlE5LdonnnjiiUQHbrrpJkaMGIFWq010mPbt23Prrbdy4sQJBg0adD7HqCAQjHDs69ozfp3RaCQUCp2HEZ0b5j7xG4yDpkolJhoNupQstI7OHCx7mZzcvP/6+hXFr5I6fIbi9YbMy6nZ9id01jQCR/9F+rBfKY9ndKF6QxF1u98g9PXnRBtqCBw7iCHzcrT2dIIVH0v+cD2GEqn9iqCtDSv+6eKxnUFWfxYjet04sm4rQPzXWlaOliY/rUagS6qGGy7TsmzrQTSX/bjpWhuKiPrqCX5VjkZvlEWw4uMxZl1J3e7XCR4/hKV7f0JffYag1WK/bhiBY//CES89bna+++8rych5BPuQaXjLd2Ef+iCWrv+LxmSjdtufMGR0kd+/qrQQ+/XDMVR9SiAS41cbA0zbGEAQ4GfZemZvDzJnR4hVh8JoBEgyCvx5jFnxuXpkaXl0W5B5Nxl5bGeYlDGP4f/3P5g4fjgHNi5Hk95Zfr+6jUUIkSD6rCvlv3k2L2LS5AfofPlV8r272H+bcc7nOK/qknlG51+M89O3nZtA/Q2cSy6FMcLpx+mPQXqqGZtBg14XQot4Vu8VRUM4asQbjlFTF8B82qaobz7Oi4nzNc7vw9wEZzc/fZd8l7+3ndvW886qt0kZMYP0W6ZhbNud4OG/IlR8QNW2V7HWfUH/vgM5/N42dBmXY778BqKBBmp3LCXqqcZ/5AOMzZ7vVWXzibqd+A7/DZ09A8+/NlG3+w08/yylbtfrhJxfkDnm0RbrmNDJw7Sd9CKGjM4EKz4mdPIw6bc8gNaaSu32V6jdvpRw7XEyRz8qjbNdd3yf7SPq9xD48oByzbahCH1GFzwH1mNsI42tbu9faDi4mcwx0utN7brjO/w3NAYzST/NxXtoD95PtlO7/RV8n+5BY7IRCzQQOvk59p4jCFZ8TODLA9g0Yeo+2oG24j0MPx6C5/112K8fjmv3Ml4eZUKrEeTvtn2SwGM7Q3R3aLg6U0uX1KZJZ/fRKH/zZDL6jnsgFqOmPoRZA50vv4qM9HQOlr3M1+++hLXuCyZNfqBFMuhSmZNO5XyM+0znp3NNLBIi6Kn87yeeIZa0juf8mq3Raka4W7du//XFNpuNp5566pwO6IdKa6IN37SM1ueuwZHIc87txNr759TvX5G4hCbobVJqLpuP5cpeVK2ZRyzoRZ/egdTGjLG507U4SwqImbMgVodlzBx5t1HqH1YKgEiCVTG8JXMRQ370aR2w/vhGPO+X4Rg+ncrls1spGwoQrq5APBQmbci91O54VQqgPa5WSn38VL7zJIIgNJYaFaA1JxFxV6IxJ+FcPRcx7EeX3Ebu06lyfcnSA/9QGL3/fKWPqAiCAA6LQCQKR91iq77Ce49FSU7PkL6DwdPYsmMpvyt4mLnPvEjFUam0/YEHfgN8c+VvlW+OOj+pqKhcjKhz06VDInGm1FEzYe8rvLXtoHxe96uv45WXFlLpOomgNwIiaPXEvHXyGkOf3oG0GydjzR5A7e43pcDzFLHOaL2zVXeN5irQaTdOBiQBLTEWpWbrEjJGzVSMMyNnplTm7K2X2trCwcaxQdDzL9AYpLGF/AgGM5ljZytfP+phnCUFNJTvlm2cmlcTxsJB0m6cLGm4rH2WDL2fZTnxqr8IE0pLcdcFqPj9WGxGTcL2su4ODfn9jC3cOO7eCDPnzkh4T85EJFZF5VygKnFcJJxtb4SgN7VSCmyk4YN1sjDDaZWaRz1MzdYlxIKSjZGgbfp5xPtL0nPzWwSxyekZCQ3Zk9MzMQ5+CGdJAeHqCiL/dCJGJKXE1pSqBYOJzLFziHprce9fTsznRjDbZU/kU8/X2tKIhQIIOp30INDqlRP6+iKsN4zCV76T1IH/B4Ch9oisEg1wskHEYhBYMVppm9QpRUg4uXdJEZhQGkPXb6L83VSsOMLYcTlcf+N4Dh+pVZjCq5O6ioqKiorKuWXntvVnZU/4TRMQ8Wu+8tJC3K6T6FLakNwnj5qtL8m2j77P9uHevxzXugVojBYyxsxSBJ6OYQ9StWZewnWMxmjl2IKxCDoDCFpqd/4ZkAJh975ixJA/YQAd9dZivvKnBE98hv1/RuMt36mwy4wLa7X2ejHoQzCYyRijDJIdw6fjLCmgen0RGpMVS6yBZT+ztHQNeUfHfyo+YebMJ8lb/jbFY02K1rnJPfXkXSO1B8huHGYtvyv6nawaraJyoTmDAqlzTywW47HHHmP8+PFMnDiRo0ePXsjhXFDu+MW9eDYvUqj2eTYv4o5f3PuNXm+321soP7s2FCGKIhm5+aQNva/l8QRKzXGhhERqhhpLMjVbl4AocuKVqZJvLqDrPZEJayNKQ/Y1YXS9JzabbE1SoNrYNxPvtTlVpVAMhyRbgt1vkDZ4CukjpqPRG7HfMKqFiXtVaSFRfz0ag5GMnN+iT+8g+/LJStEjpuP5YJ3cCw3grnYqMr0Fe4K8NloKjPVagUFddCzNMeMPi0xYrTSaz1vl54TfIPsnx7+bDp27nNX9V7n4UOcnFRUVlYuTuK4KfSfTYcZq6DuZxYsXsnPb+m98jdZUo9EZmXxnjnytxc8XULTgKdzVksevpXt/arcvhVgURDj+4l3U7nqNtMFT6DhjNbFgYrGqWMDbYh1TvfF50oZMJWv8XHTJmWSOzUfQ6qnd/Qa1u9+UfIXTOrSqbh2pOYGtx1A8H6wjWu+kZusSare/IiU1/G4EvQHBYGpVoVoMtxIkh3xSP/OYWXjDQsLquHq/ZHheWPg4I8ffRu7KIMa5HkavCFLvuIalB2HHkQjjsnUsGmaio8PC74p+x63jRn3je6Sicr65oBnhrVu3EgqFWL58OR9++CHPPvssixcvvpBDumDEdxz/8ubLVDSqRp9JGe09U2ewaOEzsuecLjmLqK8erclG5fLZ6NPbY7myV6NiYDU2o0AsJBLZ/zpeYlizB8niTnE7pVOVmjXWFNIGT1GUz4ixKDq7g8qAjjElAp6GBpKSk6kPglj2e7Q7XkMwWuQSobp9xbKxfHLf22UDecGcu9DSAAAgAElEQVRoRoyBxmzHva8YS6P/ccRdReatUhm2wdFRUjV0HcNmFBCDIjYD+GNRot5awjWJlZvFoE8hfmE1CDy5K8iaTyMccsUQxcRiDk4v3P8THdM2BiivimE3aQgJJkIaI0nW9KZSptJCUm1GVr9TyvWnqCCqXLqo85OKiorKxcm58JyNizP5u/XH//l+wjWSo4b1x4PgR31ZvHghhz7+kHe3biKjmWeua90CxFiEzFsfw9g+mxOvTJV6bxvHok/vkLgCz9EBc9fecsmyLqUNyX1vR2tNpXrj85iv+Ak1W5YQqZOyzp5/rkWblIH5qt6ySFdzkS1rtz40fLSV+v0rpGsNvhed3UFV2XxsPYZy2V0vEDxejnPNM1SVFZIxaqbi9WLI3+pYBb1JtmBqreqv2+Xt5H8XFj5OYeHjvPNOGQW/W0zFlx9jdTj4v81hKpx1dOvSlllzp6tBsMpFxwUNhN9//3369esHwHXXXcfHH398IYdzwRl40whGjbkNj8fzjV/TvDTInupA0IAbkai3Bo3BjGNkk4q0s6QAxBhtkg0sG61rLGFpYELpH6lzVeA5uJlYwEv1+iLQGXCWPIOg1ZKRM5OaLUtIGzKlZfnMqqdAo8XecxSp/SeS2jgu09GD8mtcG4qIemvlHl0A5+qnJdukuKeeoMF2zUCM7brjKntOLvFpXoYtZXRj6Ha9wLIx+iaZ/rV+qra+hNaWlrh8OjlTtgXQmu0YoyJLD4TlHuHuf2xIWAKdnaFh0XAzO45EuKfMT1RnYtbcpxCB/McXcOyk9LBKHXQ3OruD3+bPZ8YsA5dfPfBb/wZULh7U+UlFRUXl4uRsdVWgSTX63a2bcDRTcq7e+DymDj/GPnQam9c+g2P0o8q1z8gZVG9aJP8t4q5Utov1Ho9r4/OKMmXXxuexZg+k4eBmxEbtu6jPTfWGIvTpHTBf8RP8/3lPGeyWFmLIupyGg+9i63GzbHcpGCwY2nTF9++/y8F4cyeQeJtbav+JmDr1IHPMo1SufFwOwDUmK9bsgQSOHcTcNXGQLYYC8mfS9Z7IhNI/siwnoujzHZwzgL4/vZHPvviKJLMOtz9Khy6XM2RQL/66M8BnR06Q0aUtSxbnqwGwykXLBQ2EGxoasNmaRJa0Wi2RSASdrvVhaTQa7Hb7Gb+XVqv9Vq/7rmltnFs2rWHpy4uorPiCrA6X0+un/4+dOzbj8QfIyJkpWy55tiwixXEZDWFRsUNp6tQDrTkJK36W5YRa9HrkLH+HWFCanbXJmdKEfWC9XGrcarY1LJmmp/TJa3EsXHNcDpilh8AA+dz6/Sta2AhUb3weY7vuCEaLrBB9ai9xZP+brBijV45/NOSWamgQtQkndFuPobJPn7dkNll2gZdHNfUIPz3IxF1r/bw2WmmfNHeQUS6HLrrZRBsbTJu/kAPl7zFv/ktobyvA1KkH3vJdjYH2SQryH2Xqw09x66ixZ33PLzYulXGeK850fvq2c1P82pfCd3spjPNSGCOcfpz+QASz2YzdbsKgjWDRnV0Xky8SQ2uwUx8LYDYHsJu++aP/+/B9qnz/OFees++//3ccjZlPkNZK6cMexLXxedrd8xIRf0PCtU/E3aSUe+o6xZo9gJDrmEKsSgz58XywDjEWI+vWOYo1irlrb/yf7ye9cd0TH0dGzkycJXMRw2F8h3YTcVeitaYR9dcTOvGZXC2XaNxxP+P4eIlGEKMROv6mSf/FW76L2t1vYM0e2CzINmPq2INI7Qn5M1mzB+EFckuXUl9XR7cr2jE4ZwBbS9fw6jDomyfZZk4ojeHSOlgX7xfOs7H3WC13z34MQA2GVS5KLmggbLPZ8Hq98r9jsdhpg+D4OWeSMY1jt9u/1eu+axKNM5HHcFlpIbGQn6xxjytLg4ZMo/Lt/IRG6xF3JfWIrSo8CwYziGDI7CJN2EGffI3WxK10KW2Ihf3U7StWeNDF+1egSb3aW74La/YAqd/YZJXLbuJjTx/2IDVblyCGmt63uW+fsX12Y3+vcrHTt6OWercbEYGk//0ZzpICxKAPjSUJy1V98B3aTf3+FWiTMoiFYhw5xdcu7xo9kZhIzts+vCGwGcCkhbvWBkgySo30edfoCUdFDr15nCt/1JdjX3yBvnYJpk49WuzivvKHQgzR0Dcuz7qUf5vfZ850fvq2cxNcOt/tpTDOS2GMcPpx+iPg95uImmvwA1GiZ/VeIbT4gjr8/gh+vx9P+NyM82LiUhmnyrnhXHnOtpZZjrqd1O0rRme2JV77JGfJ/07uPV5uJ4uPxVe+E9vVN9LwyQ4yc/MxderBiaX3kTZYWVmXMephuULO2D4bb/ku3PuXS3otae0Rg37MXXsRqW303RUEBJ1BsT5LNO74+is+Xo0lCQRBsVaLB+ye98sQw36y2nXhhht6sXPPTszd+iuy2lprOgGdmT+89Bi3jRtB35/eyKvDUCQlJl0TYNE//kFVSGTaxgD5/YzkXaPn1WER7i8sUgNhlXNCLBbjiSee4LPPPsNgMDB37lw6deokHz948CDPPvuspJGUkcH8+fMxGo2tXu+CBsI9e/Zkx44dDB8+nA8//JCrrrrqv7/oe0jz8uasDpdz+8R7FEFUol6YjJyZOFc9RcTj4sTS+2SRq6Re4xD0JrTWFILHy2X15XD1cQSDGbuBhGXAVr2AV9AghnwEjh3E3nMU/s/3yw+AUwPS5mU4WmsqzpK5mDtdqyjpEbQ6vOW70FpT0aW0wf3X5XIvTGtiEuHqCoUZfDyLHO8ltpu0CceflJxMvTeA559rEfQmRK2BmK8e74cbsRtFPIA16MSv09PRFmlxjfZJGjola1g0zMSY5T5cfkg2QSwGL440U/xRmDk7AogiuGuqcYx4CK09A2dJgfyQi9+bM+1TUrk4Uecnle+aGkl7hq5dUkm1NABaDGcZBKuofB+JP1/P1p6wtcyyLqUNDQfWccvNOezcvEgRcLvWL0CMSGJXUpCYStTvlTfhBaMF248HkTZkiuTn2xjgRtxVsmZLcu/xWLMHyLaRgtFM3b5ifOU7W1S1BY59gs6eCojEQn7sPUcq1menjtvzQRn2nqNkHRPXhiIErR7HiF+3WKt5D25m2LDRTH0wX75O96uv4+XFC4jWV+NdnY8zJGI3a/nZhNu4bZz0/X525AR985qSKsUfhVn2UZg145XuGwDjsnV89taJM77HKiqJOJ1+iyiKzJkzhxdeeIFOnTqxcuVKvvrqKy6//PJWr3dBA+EhQ4awb98+brvtNkRRZN68eRdyOBeERNnexYsXAk0TfWs7lmI4SN2eNxW7kK4NRZgMeqJhP861z6LRGxXHnSVzySsJUZyLQubeK5gVnndx4YZ48Gvp1kcq9Vn1FGIkhD69vazELEYjiCE/zlVPI4YDir5Z14YixHCI1BsnUb2+iJotS0jpfyfu/ctbEWiQynJc64twjJguP2QQY9h7jsB3aDd5JT7F+CesjeAJ6bD/z2h85TslEa71RRgc7UkLHGfZWEvTuav9HKsTuXONnzfGKCfsghsl6f+GEARnS6U+d67xs+dYhPWHI81Kp0NMKF1MpN/9iux183tzJn1KKhcn6vyk8l1yahCcYlKDYBWV0/FtPGdPtVy64YZebCotJCNnpmL9k9z3dqo3FDH1wXy6X30dv5//JGLYj2CwNFacJUuBb8iH1paO1mxtsRYzXHYVuuQ2coCb2VgSXbevmOrNL+Ja9xy65DagM2DvOQrPB+tabKxLfr9zFUKl8fVZVWvjXl+E//B+6v+2Ulqr9ZtI9YaFjUG3Xyq3DvpJdrThkUee4H/73KT4jj795ABhTxVZFii+tWmd9IvV77Dqp9dz67hRdMhMYe+xoJxQKNgTZGmOWZEhXppjZtrGAG1sAt26tD0Hd1zlYkMQoug1320lzun0W44cOUJKSgqvv/46n3/+OQMGDDhtEAwXOBDWaDQ/eFP5/6Z8uHPberQma+Kg0WjBMXx6CwGrwNZFTL73IX4//wkcox9RHM/MnY3znSflMmCrAbxRPZk/m52wTDml/51S70h1BbqUNoBA1vi5LdUQ0zoQqXeS1dg3GyfeH6yzOxD0RsSoVJNn6thDVo9unkVG0GL9UT9CJ//dqHBdgz69gxx0G9t1x7VjKTlv18jj9xvTSBs8CWv2AMydrsW1bgG6lDaYPBVM+qlB9q/r7tAw6Xo9i/4RwhMUGbHMh1YD3hB0ShGAJhP4uI3SG2PMjFnuY834BB56G96UbQ3Otk9J5eJDnZ9UvitqImCzm7nMYaJLmh81E6yi8u1J5C8M8PLiBTQEQgpdlU2lhYhBn+y4Ed/k11pTyWon2SIOvGkEWzet4aNPP22xZkkbPp36v7/TouQ5LiZqtyfh+aCMzNzZsqaIr3ynIvFQVVqIPq1dqxvrYsifcH0maHUJx61LaUPbSS/K1wgcPSi3t2mMVmzXj8Bc8R6vvFHaoqVgyQtz+eeW5bS1odBSGdRFx+uj4P7CIkSg0htjwtoIy0ZLSYlDrlhC941Drhh3b4RZc6ef25usclEgagxErO3/+4lnSE1NDZMnT5b/PX78eMaPl1xZTqffUltby4EDB5gzZw6dOnViypQpXH311fTu3bvV97qggbDK6ZUP49liy3XDWygQVm98vtUeEWd1JQNvGsGCeY8knlQjIXzWVDLHShN6w4JcxXne8l24/7qccHUF7v3LMV/VG/FQmFjYjxgOtJDhr974PJbsgdTvX5G4Z6XeiWtDEWlD70Nnd0iZap0RW4+huDYsJFpfpcgiV5UWEvO56fibEr56+V6FWrU1ewBaayo1W5Yg1lSQ9qsm4Qf5/RpqMHW+Dk/d1yz7KMzSHGXm1xOEWf0MvPZhmDdzzYpscWWDSKcUgWkb/Oz4MsohVwyLHo7XxxSfq29HLe7qKuz/+3Nc6+bjGNn0cKzfvIj7zrBPSUVF5YdNUxCMGgSrqHxLElXZ/eEPzyFGwsT0ZlkAFJrazFzrFiCGA2SNn9tqv/HJysoWuiYZOTPlQLQ1MdFwMIAYbPLqde9fnlAUq3rTotZtl9I6tLh2uPo4jpEzqN31mmLcVWWFxIJ+jhaOQp/WAfNVvfGW78SQ2UXOYHs+WEd90Jfwu9u9aSVrf25m8Ju+hIHtZ2+doOB3i0ke8Vsi3mrGbnhTWgsZNYnb1kwaZs19Su0PVjkj0tLSWL16dcJjp9NvSUlJoVOnTlx55ZUA9OvXj48//lgNhC9mTqd82DxbLHvoVlcg6CUFQsFoSShSldmus5RJNloSZ5INZsWE3nzy9Zbvom73G4oeFWdJAcRiiJEAGpOVmNctT6iCwYIYCVG/f4Vs2t7y/SykDrgLa/YAvOW7IBYl2uDCf3g/iDGSev8c/+H9VG9YiD69PbZrb8bzfhnB4+Wk9r8zgRJ0ITGvG7QGTrwyVd4NTe49Xt4NDZ08jN1IwlKdMct9rPk0wpu5ymPLxkplPIuGmZiw2s+k6/UcGGCUVaR1GoG8a/RAU19y9PBu7ho/nC07llKx4ghtOl7Onb888z4lFRUVFVCDYBWVsyFRlV3KsOmnDVij3lqS/vdnVK99hmjg/7N35vFRlff+f88+k5lJJskkUEiCaK0alar99XqRfRFlCxBRRAW9inXppUgRq8Z6WyWoYAw0VdBCXSuLEpZAoiwaFErVi7bRhqq9IgkgSSbJZJl9Ob8/JnMyJzMTUFFEn/fr5avMzJkzz5xJn/N8n+/3+/m44vqNq3dto+HwQfISqUc7jyW3brRk4A140aRmya8HmusTq1A7j5E6+Jq4pEdTxRIsg65QHB8JjnPQmNORAn65ek6TmhVRpZ7+P3HrJV/QH5eFrt61jclTr1Vcu05fKGIraU8c2J4zsB//OniQ3ML8SBIifxRpgOujXczcsExRSn3d5iBaq10EwYKTSm/6Lbm5ubhcLg4dOsSAAQP43//9X6ZPn97r+UQgfIrpTfnwicX3ydniqGhU61sv9JgklwLI9kDOqlLGjBzLihXLkNS6OCVDR2Upkt+jmIhjPe/a/qrcrQy5WlF3lWDHBaJaPWq9EXvX5OrcuyZhuXPG2NvkINj51guKybixvBjXR2/I/cBRvz3J75Ezz2lDr5fFsjSpWVgGXUHH/q2otDoyr5yr/G4x/cgdkHBHs8NHr2U8sUHxotFGRg3U8twUE7dWeJier5X7qnVmC0seXsD0rknej4aDLSY+Pdj6jfytCAQCgUAgSE6yKrvohnnCjGtmDrqM/oSCwbjzVe/aRtmyR1AlSSyk2fvS1uyIW/s4KksBFZLPDXoTjspSzOePRqVPfB5NWjaeT/cRamvsFt3SG5H8XjprtivFSCuWEnY5adm5koyxvyDlnCHUlRRG1mMT7urRYxwRVlXrjIRcrag0WjkLverpZYpAuPHI56RlZrOnrpOiYQZu2eJRVNTNLPcwacZUXNI7XWKszQT3RTLCqWlptARTKKw009bcRFpmFtqh19Nctfxk/bQCAZBYv6WiogK3282MGTMoLi5mwYIFSJLExRdfzMiRI3s9nwiETzHRHcdVTy+jwXEMlc4oeyH2zBa37Vsn++tCt5BC06bFtP/tFbRpfUjRqNi//x2s4+biXluExpIpi1hp0rKxDZtFy/anEnvedU2+sUFy2751cX3IWZPviZQmhwIKr+JoZlr2zjNE7Ji0VjvQHcQrvI1TUuP8ju3j59FY/nBX5jki6qC19cU++W55Q8B94K3498X2IxtSSDXp2FPnj9vRtBpUqJAS7naeZ4/4dUaD4ihD8zR81iphXNzJOQP78fATotRHIBAIBILvEklVoNP6JHW/MJ31c5xvvyiLWfkO17Js2aMc+Ojv7Hl7FyGNDmO/cxXBrnPvGjrer0DyeVCnpGEZNC7GizcF83kj6PxHFSqDiVBHM5kT59O66xmsl0xKUOW2FJVaE3H/sOdiyLkA9yd/lYWwnHvXyFV40R7f2EpA76EaNJYMAo7E2WYp6CfzyrlyUBpVq25wHIu7dt7c/8d1W7bwcoGa3480cGuFh89apYgeizaNTRW7WPzwQubd/TA2tYf1U7QMzbOwp87PzI0+On3dnueBliPknjHwm/iZBT9gEum3nHXWWfK/Bw8ezKuvvnri5ztpIxN8LQJh6HNtMbnz1mAadxcrVizjZz+7lI7tZXgP1SCFgklLasI+NwPu2UK/OStob2miseELGtYVoTKYMF84htx5a+hzbTEqIhOTFAzQtGWJfF7voRrctdVkjL0Nlc6A73Bt97iaDye2OWo5TLDtWNxrtiEzkfwe0OrRpvdDCoVwbCuh9a0XCbU1JvQ2TiwO4UWlM0BYAhX0m7NCDoJ7e1+ovTEiuhUM4utsY+YGD28eDBIISbx5MMgtWzzM/Q8dniBcVx7/WtGwiNdYbFAcfXzuWf1pavoXe959QwTBAoFAIDglhMNhHnzwQWbMmMGsWbM4dOjQqR7Sd4YbbrxNsW7yHqrBWVWKOuBBY04nbej1OLaWUPf4NJpfKyNt6PV4Ptsvb/i7P95Ly86VhNxOqqo2097Win3CfIKtRyPB7s6V1D0+jc6a7WRPewBdZi5ZBfeQPnwW/eY8xYB7KsieVoT38w9Q6VOwXjIZld6E1mon7HNjGzIzIkK6cyV1JYW07FhJ2OXEPvHX5C0oJ2Ps7bg/3Yflp1dgHDAIlUZL+vBZZE8rQpeZS8bld+D65xuK79e4+VFQqdDa+irWb9Cd8Y6KbLXtWyc/r9IZ465d4OO38ZxXQMFaNzdu9tLoUZM6+BoyfrUJe8FvaO30AdDHrOblKVpGDdTK4qJrphnoq+3EV2ShfEInxg9f5fJRl347P7xA8BURGeHvAMmUo/fvWcUdd9wl++RpjYmN3aPG6c69a9CYbdhjynOaq5ajt+dhzh9B5vh5NLzye9QpqXG7l4Yf/YS2vWuQAj4aNxZjvWQStiEz0ab1SVzGk5qF5HMlVbNWG1IItTVB2I9t+I207npGnqRjj092fpXehBSWIOhNKCCR7H1qgxlD/3NJ/eId1hSaGPOCW6EaXTzayPR8LYv3+PGGtdxa4eGgUyLVAL/8uZ7p+VrePBiUe4QDIUkuhQ6ZLbz6aoVcCi0QCAQCwbdNbz6aP3QS+Qv/93/fLT/XErVNGjOO/fvfobFyGZIkyV6/PTVSmrYsIdjhINB8mB/dNJP04bM4uvpOWSU60HJYfm/bvnWRrG5GDkHnMayXTMQ2ZCbt+9bTXLVcXreY80fIG/veQzU0v1Yma7S07VtH2O2k84Nt8toNugWyNOZ0wgEfjqrlhNqbIvaSoSD2KfcScrUqss2RrPVWJL+bo6vvJPXS6QSaD+M9VIOjslSuPkx07RolE5o0Gyk9Ku+yCu6h6LdL8DpbGfsinGdXUzTMwMwLdQzN03DQKXUHxoVGflm9+1v53QWCr4oIhL8D9KYcHeuTF1VDVBi7V5ZiGzYL76EaOvZXkF2Y2AYpWgqjUqtkoaxoaU3rWy/SWbNd2dtbsZT2fetRp6Th2FqCfdKCuF7csNcVJ+zg6FKzNl8yiY73t4IELTuf7up3SaFhbRFaW1/ShsxEa7UTcrcrPIOjn20cMAjf0X8hqcH0k8GyMrXnk30EWupBo48bV9OWJYS9LjR17zDnP3TMrfIC4A1KvDTNJAtdvXkwiFkHYY2eurYAKTpo88KT7/lZ/LYfqwE6fFD2boDFb/tJtdnQjbwNjTWLBUUlACIYFggEAsEpoTcfTUFyf+FkIpZzZhfgO1x7HEXn7v7i2Eo5XWaO7BPcs9zZ0P+8rmRFLmmXzaC1+tk479+mLUtIH3Vz0iC8tfpZ0kf+FxpzOiqtHkflMjK7dFcApFCQuscjzh9RB42WnSsJOOpRp6QpNFkclaWgjlguhX1u2tvdXHvVGK6fdat8baLXrnrXNkqK7+0K8t+Ue4FTLBYs4U62XJuicOMA6GtRKSrpoirTAsF3GREIn0QSedediHpwb8rRsfTc6UzN7INZq6KlchnGFEucCBZ07yJGzykFfHHHeD7ZF28LMHlhxFJACiOFQwqvuvQRN6Exp9O4sRhz/sguNevD6DJzMOePpP3djV1lQ0WKm4L5/FGyqFfTliUASCE/IbefxvKHkfxedPZcMkbPwZw/Au+hGhpe+R2d/3gdQ/9z44P1zY917Yo2RtSr/V6sRg2d3iBPvufnlz/X88Ft3arPwbBETqqamzZ7eGqCkY+bA6z+QMXLhRExiN/v9rH6g4D8OKp6GBx2C+b8UZGLNXYuxY+tEIGwQCA4KbTE6/N8IzS4gnzh8H47Hyb4RunNRzMZarU6LgP4XUSj0Xzr45xz2zwefex3hFzOE1J01mV0B8Vpg2fQvP0psqcVxa2hml8rIxzwYBl0RbfgafWzNG54CCng61J99qK12mnZuZKUHuspy0+vwPXhTlp3P0c44AO1BvP5o2Tx0UgGuh6V3iSPJ5ptPvL0rQl1VBo3RHorVRoduXdvxHe4lhUrl2E0Grn8yqny95489VpWP7Octr0vYzqwhfUFaobmWTjvyc44f+HVBREx0UAYHh3TXW69py5EXp90rNbUuGvuDoYxmUxojEZMphBW44mHI6fib+RkcLqO+/uOCIRPEom861asWAYk34WM0ptydG8YjSY52J42cXDC0uOozL73UA2OquWoDKa4YwItiXuPQ50t8uOcO59T+PVKoSCSz43ro53YJyqzxRqzLYFy4UJadq4kffgseZe1ceMiCAXR2voScrfHfT9DTj6EAlh+egWdH2wja+r9ynNO+Q0tO1fSf8FG6h6fSh+zijmX6Nj0LzUHHGGefM9Pm1eibIKJ56aYKFjrRquGGy7UoVWreOo9PxuuSZEn9E3/CvJyYQ9LpSlQWPkidAXChpx86tcfxI8mbrxOb0gsNAUCQUJ6BrwebxBP13NnD0wnPaUTEswrPUk09xyP6NzU2eEhQ9z1T3t689FMRjgcpqOj45se2tfGarV+6+P8zyFjuGLM36jcWp7QklFjyehWdO4SA40KZ6WcMwTH1seTBtA//dl/8lHNdgBctdXYJ/5aWf12xkWRDf22RsIBX1yFXaizhdTB10TeO35eZN0UPVdMCXRP1eqgM17DJept3G/OCupKCmUFaS6fy6qnl/OfQ8Yojr/lF/N4puR+Xr7GIK+LDjqlhI4bn7VKZJg19LWo5Jay6zYHcYZDPPvsX+KSB340eDwgBQJ4PB46Aif+e52Kv5GTwek67u874pZ4kkjW5/vS808fNxBO1NMS62EXpbdgO+jpJHP0rfFqhFuWEHa3dSs5Gy1xE2bsbmIU3+FatLa+hP0eQEocYNtzSb10ury7qbX1JX3ETTi2lvSamY4+lvwe8rp2JB2VpaA3kTH2dlnZMOoJnD58Fu1/eyXxOR31HF11BxY9zLlEx8sfBpRy/xs8PPleOwPSVLj8cGa6iiffC/BiTYB2n9JeKZmlUltzE2kx37tv3pkcbDHF/Y5ioSkQCGLpGfyePTBd/rfJZMLjiWRPBmZ4AE2vHsLRADjR3HM8xNz0/aI3H03BV+O8Cy5ix5vbyehhyRj2usm4/DYsF4yWe4NDrtauzG7EkUOlNybROjHSP+cM+uecQVXV5rjWtazJCyMZWq0Bld4U56oRCXyL8Xyyj1B7k7xucn2wFfvU7gy0wrEj4EGTmp3U7kmlN+Lcu0bWloHuVrxYdu/aysYX/kCnN8TcKq/cB5zMX/jcs/rT1BFg2hY/7W1tEfuk4bNIM2eKKjrBdxpxWzxJ9NbneyJE+zKsVisVm9by0vNP88Ti+xQl1r0F21qTBa3VLqsRBpoPo03rAwEfFW98KAfR2nOG0/6/FZHgNeiPlN+cPzqppYDns/0Encdo3LgI6yWT5dLm5qrl2IbPRmNOR52SRqitkX5zVqDSaGnbt65XUa/ux7nyjmSkZOdhua+5+aTQ0U0AACAASURBVLUywn4P6aNuBpD7c0KuVlmQQm1KRW22kXnlXBrXFbHpX0FWFygzunMu0fHUe37q2iOCWNdeoON/RkTKpa8r9/D73T4WjY4sRpNN8KlpaUihIL7DtbRvL2P2L+5K6hUsFpoCgQC6g+Af51hQ6SL6BOkpndiMkYDWZAKPJ9Jb11sADJEg2OkN0eq2fGWfcjE3fX9I5KP5feKrtpl9rc9q+ILsq34bb8m4qVi2gIz2Bqs0WmWPbuk1OCpLsU+YrwiipXCYqte28Ou7H6Rqy7qk1kZ9rnqQhrVFSRw03ARa3LJoqNqQQqiHzSUgi3IBhL2dGPMuTKjhYv1ZAZ012zGfM0R+b89WvN27trLmyQd5sUDL0Dyrog+4aJiBmzZ7eG5KrL+wl0kzRvDnZ9eS++tybD2qB+vXH/w6P5FA8I0ibo0niRPt8z0eO17blDTr21uwPb5gBq9XLMU+eSE/uukPkUmvYilXToj0fERvIstKHqLPVQ90GbHfLo/X1f+8SB+w8xg6e24kCP6/9+LEH9r3rY+IXQ29Ho05HUdlKekjblIEv4m8+poqlmIZNE4OKJsqliom4kjJjlf+d9B5LGLB1HUDShs8g6bNj6HS6eWbzdFVd5B55VxCrlYsBg0HHEFFRnfNhwFe/jDAhmuUog7nZ2mYeaGOlwtNTFvnZsxALUPzNEw9V8t15R5Fj/DMcg/ozdSXFJLd/wzuTJCpFwgEglhaYkqeAfqYtaQYOojN+qZo1YSOEwDHEg2CRUArSOSj+X2htzXQybj3xgbZ1nQ7vpCEbfx8pHUPEOxwcHT1nXKPbuql0wn53HRsL4NxcxW9wVF8h2shFMA2bJaiv9c2bBbNlaWoU1IpXfI70BoU73XV7sb59ksgSbTsXInGkoFz7xo8n+6Tz2E6ezAacwZhv4dAcz2NGxch+TxJq/g0lgz6375aTlaknPXzrjHVozaYybj8Dsz5IzAN+CnNr5XJ67GO7WXM+sVd8rz1l6cf4y8F2rg+4LlVXsrGG2n3q5m2WUt7ewdpmVlIF05ix5vvkHvGwITjEl7Cgu8y4pZ6kviqfb49Wf2nsqRZ396C7TvmFQGwffMjBD2daE0WLsi/gP3736FgzCB5VzXkdUdsk5rrFSXSEUl+L6mDr5HtAeLUEycvjPQZa3U0Vy5DpdUjocb59kuRrHFX+bXaaEZj6xszaRuR/F463q+g/W/r0WXkYhk0DndtNa7+52HOHxGZxNOy5e+kNlmRgkFFH07rm3+Wvf4g4iUc7HDQtucvmC8uxPr+ekVGt/htX1yGODqZR6X+233I9koWPXjTz6KwsoO25kYsBg3Dr5zB7b964Gv8ZQgEgh8iP86JiBn1MXffZo+X+RUIfuj0tgb6uoFwz/ayo6vuIHN85LM0lgycb78Yl9VVafWMHDaS/XtWda2blpA1OUb1uWIJKp0BrdVOv1uekj/Le6gm4vs79na5fSy6ngl2OOI+q2HDooTuHWGfmz7T/0dph+Rzx1XpOSpLkUJB3B/vle0yW3aupN8tT0Wy1iWFCiumoPMY9SWF9Mk9k1m/uIvLRk+U56xjjlaG5ilFnYbmaahtClOw1o3x8vmYLxiDres1KRSk/olXWbliScRZY2z3Oti1s4yHihd8rd9NIPgmEYHwSeJE+3yPR0P9Z0mzvr++/5F4+6SNxaCCyaMvRGuyMG7cJO6YV5Swn7hs2SOoU9LIuPx2eVJt3FiM5I9YGyFBZ812TAN+qrAHiB1HqL2JnHu2RMS3tj0BSOiyBhAOeOMmcH3fn2DrUlpEn4J90gJFEG8a8FNadqyMZJa3lWAbfiPeQzU0VSwh7OlAY04n5PfIitLRMUTRZebQtncNmePn4f5kD+EQioxusp7fA44wECl7NuuhbLyRmeVe2n/0H4SOfozT5aRPzpnfaDmWQCAQCAQCJb2tgb4u0faykKuVL577lVJQSq2J79GdMB/H1hL2739HTnZozxkeSSa01KPS6tHrjfgCvrgAOdo+FhWoypw4H+fu52lYW4RKb8T6swJ5HIHmw6j0poTuHbEew+7a6jg3jvZ969HZc2U3j1i7zFjHkJ6taWn2vry0fgcenQmNFn5kj7SIvV6xEbNelbhNzGbD7QthtmYprms06xvtAy5+bAX16w+Se8ZAHipeIPqDBd9pRCB8EknmXfdl6JN7ZtKs78gxEznw0d/lrK9Ko0NlNCsC0NcrlgKwf/87il3VkKuVQDCA5HfTsnMlxrxBeOtqIkGwzoT5vBF0/r0KywWjaNm5EpVW36sCddOWJajUGuyTFtC0aXG8ovPkhTRtWkzI1YpKpSbY2ZJEQKuepi2PIQV8NG8rRaU3dSlC34vvcC2Nmx+FUKRep2c5UNrgGTgqHifU0YTqn1VUXJfCsU5JzvCmGkg4mZ9nV/PmwWDEUikEU9d5kM6fQPbld+A9VAN7VrHqhS1xv8232bckEAgEAsEPjd7WQF+XxiOfk9FVRZY5fh4tO1bKnxUVo4rFkJNPyNVKo6tVodGSPnwWrtrdtO5+DltXVrdp86OyWJUuMxfb8NmyDSQ6E63VzyrWao5tpXQG/WRN+Q2GnHzqSqYlVp5uawBI7HHcVaXX75ancNXulqv9jq6+E9PZg7sdQyqWYo5pTXNUlqIJBdi6fRu3//etGFVN2Iweyl/dQsnipWh+djUzN7zCmqtirCS3hNEN+S+Clctw7SxLmvWdPn2yCHwFpxXq4x8i+Da55da5dGwvw3uoBikUxHuoho7tZdxw421U79pG9dvVZE65j7y7N4JWJ+8gyqJTkxeyfftWGo4clCfVqFF79rQi8hZsJGPs7bg+3ovp7MHkLdhIduEDeD57T84W97vlKbmMJ3YcTRVLuwLXJUhBH6Gu4DbsdSWcwMNeV8Qg/sq5stBDLFHFalCRfdWD5N0dGUug8SDuj/fi/uSvEA7JNxbjgEGKMWnM6ag0OrR7nqTDF5H0n3mhjo/utBB6MJU/XGlk5gYPbx4MEghJvHkwyHXlHmqbwtxa4cHtl7CnqOjwSRj658vjbuyx81y9axvXTx9NaekjMHQOuQvKYegcVqxYRvWubd/Y34JAIBAIBD8kelsDfV2y+58hV5EZBwwi7bIZ8poi2v8bi+9wLdq0PmT3P4PGI58r1jmt1c+iUmtpWPcAXzz3K8znDsM4YBBqUxoZY28n5ZwheA/V0LjpUVRqVfxabeJ81IYU+blkayRtWh+A5FV6bY207FiJ860XyLj8dvLujqzxOmu2E2iuhz2r0BPCfeAt6koKIzaWI24iffI9lL/0NAA2Y0S74LHHnsQ8di624bNxqNOZtkWPobiTwkoLwWG/RGPNInfgmZQUL8Dw7mrqnyjE8O5qSkTWV3AaIzLC3zEuv3IqXq83YYn1nNkFiiyvlEA50JCTT9DTiTrGLzjZTmKsr699wnwayxdxaMlkdBm5qPRGLIPGyf52Kp0BSZIinxvyY/1ZAZ5P9kX6eY3mJDL9JsUNp6eAVqTfWE9WwT2KsWWOn4dj2xNI4ZCiDKi5ajkaa2ZXL7Ibo1aFVSvR2A5WAwoFaICcVDWdfomr1rtp80Femkr+DgBqFdx4kY4/fpiC860XgIhlkzXdzpzZBTQe+ZzUzD50drQTBjmbnjZ4RqTX5iT1LQkEAoFAIOh9DfR1ueHG2ygpvldeN5nzR+CoeDyhbkqsfZJHHSI1s4+8znHV7kYKBRWewM1Vy0kbej2eT9+BPauoP/I5aoMJtdZIyNXRa7YXuirceqg8N1UsRQr6FYF6IptLV211wqq8jqoSVr2whYIxg8hd8AKqnmrOPTb96z8/SG5hZJzpI2+JJFBmFMdlfkXWVyCjklBrTm/tCxEIfwdJVmLdUzW6twBUUmtlOf9Ac31SX19X7W7Zjkil1ZM5YT5aq52GtUXoMvqjAoWZe097peaq5RhyL4i7gTRVLEXyexQ3HEC+4WjSskkfPjup53DY20n2VQ/GBciNG4tJ/dkkVP/YSKouyHNTuhWhryuPyPvH2iMFw/BfF+komxDx3gyEJAyLOjBqVVx7gY7VH6rRDbuFTHMmza+VoQp4UGt1mIbOkXurOyqWYh00TmEdBZByzpCT0rckEAgEAoEgwsloM0t23lVPL1Osm3T2XNlBw1W7W7H5r9IZybj8NrRWO60VS/BWlWIbP5+2v65LuIHf/FoZqRnd/bMWSyptjmNytjcuiO3K9kJkjeR31NG0aTFhnwu1wYykUmO9ZJKs/Ny0ZQlZBffEBd/N20oTrqMa2lpk/Rjn3jVySXfbvnUEHPWo9CYuzs0kd+CZPPCbOxSqz9E1W9RNJO/MM0W/ryAOSdLilzJO9TC+FqI0+jQiqhodxZw/Mq582VGxFCngJ3vKvaSPuCnS76szJiy50aRmRcppxt5O3oJysq96kLY9f4n09RpS5BIiz6f7ZBEJ98d7adm5kmB7I67aakxn/Zxgy1HCLieN5Q9T9/g0GssXEfZ5UOkMPcY7gozLb0dr64taZ8CcP0L2B+45NingS+qpp/q0mkxDkOemRBShdRoVowZqebnQRNm7fgyLOpi6zs20c7VUXZ/ChgNB5lZGguQ9dSGsBqh1SDx5wEZw2C8x54+SVRRNJhO28fMVJUxZkxfi+XSf/Dhz/DzZLupk9C0JBAKBQCD45plz212K0mvT2YNxdK2jUs4Zgn38PDRmG9b/N4XcuS9huWB0pC948j0YNCroUo9OmOF1HsMXkuQWKuPYuagNKZh+MpjmquVxrWZhn1vxnKu2GsvFE9GmZmPOHwlBP+1/ewVAdtVofq1MLnG2DZ+N1mqXkyKxRLPFusxcMqfch6tmO43li2je+QwhdzvqlDSyCx8gb8FG/P9xCwuKSrh81H/i2lmmaD8zauHpp5fywXuVIggWfC8RGeHTiJ4WTSk/uQz3P9/EsamYkNeN1mThinGTqNy8tkvVMOJFJ/m9CUtukCTsCXY1W3auRPK5CXaVXkd7U6K9xpk9MsMp+SORDgToN2dFpJxo6+NIoRCWi65MvoNZGfEFTBs8I6ERvSYtO4lYVy5tzfW0IyVUhO70Q26aikfHGJl5oQ6AvxSaKFzvJs2o4i//0qJPyyZ79F1x5/7RGWdx7NBn2JJkz5WP67+SPZZAIBAIBIJTQyKHj6Fjr2R/Vzlzdv8zCLmcdH6wjfZ961EZUgAVks+FSmcgEPArWs+i+A7XojGmyBvpEFlTWS6ZRGfN61gGXdGtNq03YcwbRMpPLpMzrmpDCmGfG/eBt9DYfkTnP99ECvrQWLMIudsJu9tQGVKQwjr6zFikEN0y54/sda0V1Y9peOX3aMypqNRaMq/sYVE1di473oz0+wrVZ8EPCREIn0YkmsDvuvtBRQlR9a5tvL7zNdkiSbYyyh6oMHs39DsHz6fvJC2Z1tr6IgX9suy+73Btwl7jaLly5rg7cX+8l7Z96yIZZZ2J9n3rQaOPTPRtDRGT+eGz0ZjT0ab1kXccwz43jq0lhFytkecDfsznDYvrKW6qWIJl0BWoP/WSGWpKqAht0cOiUQY5CAZkv+Cyd/10+v3k9jHQsO0xmPgbRe/LgvuLeOqJJxIH4D2sBzSGlJPWtyQQCAQCgeDbobfS6xXLi3mttUVhheSoLMU29hdorfZI33DQHd9PXLGUkDdet8U2ZCbt+9bTsb+iW/izy7WjubIUlc7EhCnXyraXTy0rxuf4XKGP4qgsJXPifAItR2h/b5PsS6wxpmC+eBLpw2fh/vRvCdda0bWLIScflVqFfcJ8GtY9kHDtV7f2MwA+eK8y4bXZ8GoFpUtK+fjgUc4Z2I/598znKhEkC05zRCD8HSWZVU/PCbx61zZZ2Cm7/xl4vR7sMV50IVcrap0Rz6fvoLX1JXPCXWit9ojwVFo2zr1r8Hy6Tw6QTWcPRpvWh5C7HVTQVLG0WzSrvTFpuTIQly12VC1Hnz0Q39GP43YwpVCAusenyX566cNnyef0HqqRy36iu6VaW19Szr6Mzn+8jvWn4zi2f4PCM3hPXYiZ5T4CYRU3bfZy/y4fWg3UtUkMtKkwaWHTjGg/sY8bK4J43lhGvcNB7hkDebB4IRePnsaxRhcvPtPDq7mH9YCzqpS7FjwogmCBQCAQCL7jJFpPAQnXWK+/voWsqUVxfsItO1fS75anMJ8/ms6a7VgGjYvJ8KYg+dyk2fsm2UjPJdBc3/W/keqytMEzSDlnCHUl06h+u5rzLriIAx/9HW8wSPa0B5J+vmnAT/HuLOOl9Tuo3rWNFSuW4R3wU2zDZ+N8+0XFWivqZRwdR7TlLJrcSCS8taCoBCAuC7zh1QoWP/Agfx4PQ2da2FPXys0PPAgggmHBaY0IhL+DRCc367i5smDTihWRUuKeQXDP49rWFpHWwzZJkVXdsgSA9FE3075/C5012+NErsKeTghHvHslnxvPJ/si4hH6xOVA2rS+CbPF9vHzcFQtB0misfxhpIAXXWYu6SNvkv31Gjc8hG3ITMX3j2alNeZ0wgEPprMvJbvwAQA8//cunf94HbsuzC0X62TPYIseVMC2mSkcbg/zwJs+/lzQHSTP3uThWKck9xM/Pxl+uVfHxwcOAOBHw8EWuGz0RCwaZdb9irFXsn//O9SXvEJ2/zP47/++WwTBAoFAIBB8x0m0Tipb9ggqrQ7b+Plxa6xEWd3Y9ijPJ/tkK6ToBr73UA2NG4vpaHPS0aNEuWnzY6jUkTYuKRSQkxFNW5bQsutPoNLi8/spKb43InQa8CT+fEfEHzgqbFq9a5uiSrDlyOdY0u14dpbR0BwptbZcPEm2cerYUYbZlonvcC1pgyMuHub8Eag+raatuRGLQYMp/0pSfjKE4sdWxAXCpUtK+fN45Cq8UQO1/Hl8kF8uKRWBsOC0RgTC30Fijduhq38jgVVPouO0tu4dyYS2SQX30Lz5EVoql4HOSHahcucxa/JCGjcuQq21IklhpFCAQEs9OnuknCdRubIUCiH5OpN63PW5tpiGdRFRhlj5fkNOPlLAl1j5WmugZcdKwq42gi1HZaXDUGczFh2suTYilLVodOQ9OU908OK0yHMXPNUpC2lBZMJ+YaqJuVVeuWR6aJ6Gj186mvD6f1OKlQKBQCAQCL49ouukkKuVL577VSSQ1JuwDroi4RorKvKZrD0q0JJYKEvyucm+tphgh0OuZFOZrKh1BoUGSjRLm1VwD46tJahNFuyTFsivN24sVny+q3Y3Lbv+hDol4k9sSJAcGTlmIi2R3AU/zrEAsH3rZp55soz6klfoO+BMFhYV8e+a91mzppiw341ab8b44SusKTQxNM8acdrYsovgj35C/ecH467jxwePMnSmRfFcb+uoE+Xfhzu/1vsFgq+LUI0+xURLmwvGDGLO7AJ2vLYpzrgdIhNtYw+rnkTHpQ2ZSdOWJXgP1SQ3YPe62LKrRmFvFPu65POQcs5loILsaUXkLYgYtLs+3ovKZKVxYzF1JdO6sr0geTtR6UxJVQuNAwYlVYfWmNPj1BSbq5aTeeV/k3H57XJJUVTd2j7hLlz+eKGsox3dzx1whBMKaR1whOXHe+pCnDOwX6KfBIj/Xap3bUt6rEAgEAgEglNLovt245HPCXY4lA4Z04pw1Vbjqt0tvze6xjIajTgqS5VuHJWlpF46He+hGlT6lF7XOpYLRtP/tj/R59piCIewT1C6UERdJww5+YQ6W+TscvR16yWTZDeQzo/eoHX3c6j1JtmuST6uK3CPJRoEJ+LfNe+zeWsVWV1rOqtJy5rCHs4bBWoCe58l94yBce8/Z2A/9tQp/WL31IX4ycB++NF86f+c3hBfOLwAZIiUnOAUIv78TiGJSnZKlz2qMG6PksiqJ2qnFHuc1mrHoArTWP4wKq2+1/Oo9MbE2VhDCu6P/0rW5HsSZost54/G0P88nG+9gL2rBMi5d028l/CWJaSPuhlALsVR9BBvKyV99C0AspCXSqsnY9ydaMzpOCpLMZ8/muB+h5zZblt9M2emq+KEssx65OfOs6sTCmkNtKkIhCT21IW4uQruXzT/hH+XRKXpAoFAIBAITj3J7tvWdLtsBdmzdatl50rM+SMibWRvv4QkSegNJnxtTll4SmNOJ+TpoHlbKSqdETTaOKeL2LVOlGCHA8mXvMzad7gWlc6YVFyrZUfEpjL7qgeTilvVH/lc0f+cltUXwmGczQ1yaXTu1Y/jO1zLmrXFit7n9rY2hubFZ3jbnU6WPBZpRXv11YqIgvTnB8m027mxIsjzk5Fbzm6uhJvuv5eDLaYv/Xt94fDS2eERQbDglCP+BE8hCUugL5+LZ/syOraXKQSbEln19LRT8h2upbViCeEwqI0WzOePjrdN2rKE+fPvi2Q4pXCc5H5TxVIkv59Qkglc8nvw1tXgratR3FiivTKNG4uRfG5UeiNSKIjWagciHsK+IwdiXjcpTNvlnuHyh2neVoomNQvz+aNx11YrembaHI1IBhjzgpsz01X8zwgDOalqwmG4abOH56aY+M0QvfxvWUhrg4c2r4RhUQd5fdMpWlSUtK/lREvTBQKBQCAQnHqS3be9O8sIthxLEpDW0/nRGzjfflER2HqrSjGoQ3QAdlsaDa4W8u7ubu1q2bGSpk2LCXtdkbVOsHutA136LG+/qGhVi+I7XIvalBpZCwW8HF11B7ZhNygCcoBge+Nxxa3UBhOlpY+QVXCPHPw7KkvJnDA/0odcsRT/F58Q6mwm3KP3OS0ziz11nXEJg7y+6UyfPplXX61gQVEJ5rFzyS3sWh9ue4z/2q6mvtHJj8/ox03338vAC0bx6cHWr/SbiSBY8F1A/BmeQhqPfE5ugsm5oaWJ1IwsGjdEBKbS7H0TWvUkslMymUy4gmDv8ojT2/O6sq0RZUOr2QxA2bJH0JgzCDqPdQenBhNqVKh0OjQp9qQ+voHmekCVdCfTctF4PJ+9h77PWXKWONjhwP3vdxSWAE0VS2h960VsQ2bKE7gU8EfKoVsO4/l0H7bhs2n76zp8h2txf7KHNANkpqjo8EsAzK3yolFBIARtXolbKzwcdEpkpcDUdR46fBIWgwb1JVeTPeS6SI/OzjKkr/C71PcoTRcIBAKBQHDqSXbfbmxuSKrmrDVaaNm+guyrfqsIoG3j58OeVfzl1TcAmDZxsPx+V+1uPP/3HllT71fYGzVtfoysKRFLRufbL2GfMJ+QqzWuEq6xvBiVTh9nj+St/yeez97DfP5oPJ/sk/2GY8WtFBV1FUuR1Dq5ZDo69liFacugcXTWvE7W5Hto2bFScQ20g2dx3eYyXp4Sk+GtgqJFRQAUP7YC89geGwsTf0P43dX8s/5v1NZpRUZX8L1A/AmfQhKVNjv3rkFjtmEadxe2mGxwMnoKOxWMGYQEcpBqzh+BOX8EUihI3ePTGHplAU8seRCVzkTWlXMVE7Hk6eSKCVOo3LwWY/5ImiqWKLz0mquWk5I/kuB+B0jhxIbyadn4Dn8k98VEd06lUJDsqx7sUWp9D43lD9P+t1civnfDZtG8rZSMy29XnNfvqMNRsRS930l6ioo/Te7O9N602UOTSyLlP69Bl9Efx95nkSQnXm0qqkFXwv4KUqYpb3KMnZtQFbG33yVRabpAIBAIBIJTT2/37UTVcx3by5j36wd4YvF9xy07Dno6adxYjPWSSXg+2RdfZj1hPo6q5XKLF5IU8e2NZpDl1i8DppQUrOMXxL2/sfxhrD8rwF1bLQe8sS1naUOv7xbh6rKdbP/bKyegcB0JlNMuUwbTGnMmzrCJm3doOHSsBZtJQ5snROmSUgDqPz9IbmGC67L+IK1uC50drSIIFnwv+E6IZe3YsYMFCxac6mF869xw4210bC9TiDJ0frBV9gHuTRQhGdn9z0Cb1idOzMG5dw0qvZHKzWtBrY0TXrBPmI+ExP79Eb9h7+cfYOh3riyM1bJjJSn5I+mseR1QgVZP4+ZHOfL0rRxaUsCRp2+lcWMxKWf9XCHSlXH57eTOWyuX+MQSKbX2MuCeLfS75Sm0VjvqlFQcXUIR0WvirXkdvVpCo5JkNeiouMNzU0xo1GAbPhvzBWOw3fYS2dcuxqUy0/nPN5H83sQ3uQSqiL39Lh3by2TvQcEPhx/q3CQQCASnE73dt0eOmRhpLduzivqSQtizSq6yiwbQsfgO12JNt0eC56FzyLt7I9nTiuis2U6gObFqdKi9iX63PEWfGYvkTC5EkhHR57P7/AhPe2vStVBskK3SaEkfPgvLoHE0li+iuTISoOrNaUh+L7YhM5OKkCZSuDbnj8A2fDYtO1ZS9/g0mjYVc/01k3josYcZYE9hw3QD3iIrTw5pZfEDD5Jptyc8d04CIS2B4HTmlAfCixYtoqSkhHA4fPyDv2ckmpzDSXpzeypGJ+OGG29D8rTLqoNSKEjrWy/SWbOd7MLfknf3RiR/kv7fgI+GwwdJGzKToPMYWVPuJXPcnd2lyp/sI+xqAymE5OlApVKTeeVc8haUk3nlXNR6I64Db6NJzYqbQKOWBLFExCIM8k2rsbwYVThIyO2kefMj1D0+Dc/2Zai0OlIn3oPLT0I1aJdfeQ0MOfkEncdIH3GT3KPT83NzB+Sic9XL/+ld9fLrg0Ykv2kKfjj8kOcmwckhPaWTFEMHKYYO9DHzjc5Vj6rtoOJxb/8JBILeSRbsAsyZXcATi+8D4Nf3P8KqF7bI9/NkAbRKrZF7jqMJg6zJC1EbzYnXMlo9dctm0PDq7zEOGBTnhuGoWMoNN96WNPDWGFMSWjPZhsxECnhQm9IIdjQz96776JMzUFEyrficbSWEvW4OLZmMSmfCuXeNfC5z/oiIG4c9l6ypRex482889tAjsj9wNMFw/blBgh0OGtfej/PpG3B9tAvvoRpcO8u49ze/POm/nUBwKjnlhQ2XXHIJY8eOZd26dad6KKeEnqXNt9409WuVrfELkgAAIABJREFU5Y4cM5FVTy9DOnt4dzlOD79gXWZu4rJmSwaqkB+t1d5V4lwrl1ZD1DR+EdnTHqBlx0pFCbNxwCDsExfQ/FoZUjAQp6ooSVLcc46uHc66kkLUplTUOj32GNXpyM0IbF03I6vFlFAN2qIHV+2bmPNHyd9FZ8+NlISHQ3GCYK7ty3j49mmEPu1eYOrOzmVghgdI59ODrcJLWPCDn5sEX42on2dGmo/s0FF0rm7Lkdg5J2QyEfJ4jns+jdYHF5xxsocpEHzv6HnfPhEHiERaK3fccRdPLL6PtAQJg7DXhaNiqWKt4qgsJWPcnbJAlcacgXn4MBxVywm1NaLSGZHCkXkgUZl205YlGDQafF3WTPEexrlkjL2dxvKH5fFGv5eiZNpoBknqocWyFEDWYon6GEfLnFVIDL22Wz16zYcBXv4wQPnV0RY0PzM3LCNkzqKk+B4KphdwsKX7+kYVq6Ml6GLdJDjd+NYC4VdeeYXnn39e8dzixYuZMGEC77zzzgmfR61WY7Vav/TnazSar/S+b5tbb5tHyROL4fKYXpYdZcy/694THn97cwO5N82UlZwPPTZZqRY4eEZCNen0UTfTvK2Upk3FhH1uuScmOoE2VSzFeslkjAMGEWg5TLDDwdHVdxJoPowuM4fUS6cTbGsASQKNjsbyRUgBDxprFqjUhAM+2ZJAm9aHsN/bffPYWIx9WpEisPacM5y2fevlm5F+1J3M3PAEa67q7hGetdHDmIEa3thRSvPWEqwGNR3eMKlpqbhq30RrtZORosO47xnq6urIy8vj0YW3c+2EyxXXTH3k36gHmjCZzsNk8mI1fnt7RKfL3+bpMs4vy6mem+D0ubanwzhP9Rg93iBnD0wnLfghlqOfI0nd1iKS/cfyv9VqDUZzKNEpFKhcR1AHGuhj68dhk+lbnZvg1F/PE+V0GafgmyFRUHaiDhCJNr5fev7phEFpn5yB8rnrDh9Ea+tL+oib5IRBxGayGGPu+aiAPtcWE+xw0LZ3DSXF95Jm78vIYSOpriqhoa0l8v5RN6O12ml89fdx2ixNW5YgSVLEjsnvlccbHWNz1xjsk++mbd86MsYqExRZkxfSWL6I9r+tR5eZG2kj63LpyD1jIFa1jz11LXKCofhtH6sLTPLjUQO1rLnKxC/36pg+fTLRAry/vrGNF58RNpOCb59wOMzvfvc7Pv74Y/R6PYsWLWLAgAFxx/32t78lLS2Nu+++u9fzfWt31Kuvvpqrr776a58nHA7T0dHxpd9ntVq/0vu+bcaMK8Dtdit3J2+/i/8cMqbX8cfeBDRdpTvRyVDd47E5fwR+R53S6kiCQMsRNGabYqezqWIJ7fvWo7X1JexyYhsyEwCNJSPOcsBRWYralIoU9GP92WTCng5cB3YTam+KfIbXRSgYAElCCvhQ6ww0Vy5DpdUjBZU9xK7a3bhqqxX2A+b8UdRVlDC3yssBR5jz7Gqmnatl26dBNl3THRzfssXDdRd6Wf1WGc6ggUeumsTU/3ex4nod271P8VgbdJLS/wyMmiY8ngAdgZPyc54Qp8vf5ukyzi/LqZ6b4PS5tqfDOE/1GDUmE5ne97Ec+TeN++sIam0xr9bJ/zKlmPC4j58R1gadpAWcpFzQB41W9a1/t1N9PU+U02WcgpNPssyvu/UYeV/SASK6lmo4chD1pmIsF3cnA6I2ltHAefLoC+k3Z4UsihU9v+Rz43z7JTLHzyPkaqVtz18Uis+vVywl7HPT59piRaCtsWSQct5wOZOsTklDpdUT7miiZefToO7+nOgYCsYMIm3ITNr2rSPgqKdlx0rSLpshB+aRtjcPakMKGWNvx5CTL5c5P1S8AJPJxM0LFvLn8UGG5mk44AgnbEH7+KWjiufKXxI2k4JTw86dO/H7/axbt46///3vPProo6xYsUJxzNq1a/nkk0/4+c9/ftzznfLSaEE8X7Yst+dNwLl3jaJ0x5B7gaw8GJ2IO//xOqAmc2LEb86xrYTO9yvImvZAj93Ee2jc8DD95qzgi+d+1R1QqzXYEygnNpYvQm1KpeODSlRqTVyJjmXQODlTDZFya8emYsIYFcF627512LtuIrFKh6k2G2Xj/fJu5QVPdcbtXq4uMDG3ysvLU4z8V6WayZOu4nh5F+3h99Ed9yiBQCA4Pma9hlDQQFBrI9T3zITHqFOthNpPIHA79tlJHp1A8P0iWebXv/mRL9VqFruWyouxKWrft54+OQPlIDgaLEe1T3qeX6U3EXRGvIu/eO5X8SrTkxfSWP4wDeseQJeZQ9rgSOAabGvANmQmnk/3YR58jUJBOjqW6l3bFOtDa7o9LinRXLUciCQ9In7DZiSfC/27q6lff5DcMwYyacpoih9bQf3nB8m027luq4uGlg6sBhK2oJ0zsJ/iWjmEzaQAkADftyw3tX//foYNGwbARRddxEcffaR4/YMPPuAf//gHM2bM4LPPjn//FIHw94CeN4FooBktTdZl5hJ2OWMM4E0YBwwiu/AB+Rz2iQtoWFuURETLG+dlF2pvSqJ86CGs0YEUJqvgvvgSnY3FmAb8VGnbpNZhPvcyRbAeVWaMsx9Qa7huE7w8lV53L6PP1ze3n9yLLRAIBAKB4DtDMg/hoKczYj/ZwzYpKqLVk0QBtX3yQtizilUvbAGUwXJGhyNO+6SpYmmkfezwP/EdrlW4aMSOTQp4yVuwUV4HtVY/i0qrl9+DlMCmafLCuIyrSq3BPkE55szx82jZsRKNOR1H1XLCXhc5A3/Mu+9VoSfEq69WsKCoBPPYueQWRpInrTXbyb62iFBHE9dtfjLOX/j+RfMV38EubCYFQFhS4Q/oTvp5W1pamDNnjvx4xowZzJgxA4DOzk4slu6+do1GQzAYRKvV0tjYyB//+Ef++Mc/UlVVdUKf9Z0IhC+99FIuvfTSUz2M05ZENwHbkJm071tP3t0bUWm0HF19p9w7cmhJAVlT7lUcb8jJR6UzJt3dbNq4iLDfg8aciWNriTxhJzpWY0gh2HYsSaDspnHDw0hBX7d3cOUyfIc/wjJonBzwqg3mmJLoEXJPS8vOlQQHX820LatpdzqT7l6eZ1dHdjH7Z5/MSy34gSHmJoFAIPhuk8xDOLafN1YIK1nFXbKAOjbL+acVJfjDGjmbm/LjS2WxKrXRjDl/JOmj51D3+DQ6tpfJdpaxY3PuXYPaYKaupBBdZg7m80fjPvAWGePuxFFZijatT0IF6UQZ1/bmhoSiXoHmelp2rsScP5LOD7by67t/Lb9e/NgKzGO7g2fPp/tk204Al1otr7HOPas/9y+az1XTJys+o/CG23jxmXhv5mSbDALBlyEjI4Py8vKEr1ksFlwul/w4HA6j1UZigNdee43W1lZ+8Ytf0NTUhNfr5cwzz6SwsDDpZ30nAmHB1yPZTUBjTOHoqjsItjWgsWTIO5e6jJyEx1ut1oRqiGi0ZE25V1Gec/HFP+dfCXZaJb+HYMCLxpqV8DO0aX1RaXX0u+UpIFIarcvMIdB8mB/FCHy5anfLgl5RoYmg8xhqkwV/9Uo6OlxYrWakYJibt4b586Rgjx5hHTdvDXPvrTd9Oz+CQCAQCASCb51ESsw9+3lPhGRrqWiWs3rXNjq9foULRXPV8ohyc+UycuetxVW7m6Or7gBApwbJ51I4Vzj3rqGzZjtZU+/vXlN19QRbLhiNSq2hZdefZC/i42Vck405qjTdVLGEqVddzZRphUCkFaP+84PkFnYHzz2z1ub8UUjnDKP9iUL2vPtGwmt12eiJWDTxatuiP1jwTXPJJZfw5ptvMmHCBP7+97/zk5/8RH5t9uzZzJ49G4Dy8nI+++yzXoNg+A74CAu+Pol88JxVpahUGtnn1z7x14QDPpo2LiLQXI8jxmc46pt36x0LCLmcEcP1kkJadq6MSPFPuVfhpWefvJCjx75I6NmXmpEFGh0hV5vCy9h7qIamiqUEO1swnT242/OuspS0wTPijOEjWeCRNLzyO1rf/DOZV87FPvEusvU+Nk2T8D1gZdM0CZs+wLCfj+COnWaMxR1c9YqPz50Sr/5fGvNu+AWFo0afwl9GIBAIBALBN0kyD+Evq7XicXfStGVJ3NrohhtvAyJBX1bBPYr1UOb4ebTtXYM2rQ+dH71B6+7nIuuuuzdiHDsXlcFM/z5ZNJYvimSJ91dgGTROuaYaPw+10QxE1j45dz6H5PP0OpYoidZ/TRVLCTTX07SpmKkFBdz9u2L5+A2vVpBmVFNfMpW21Tfjqn0zbv0FkWA694yBx73uq17YwpZdNQpvZoHgm+Tyyy9Hr9dz7bXX8sgjj3DfffdRUVHxla0uRUb4e0AiHzyDRoWpK4CFSN9I9pR7cWwqZkHRoxz46O9s3/wIQU8nWpOFceMmMXLMRF56/mkYOkd+36ElBYnLc+o/S+jZ5wtJaC0ZAKSc1+1lrMvMwTJoHK6aHXg+3Uf739ajMaSg0ejQmNNJvXR6XK+Nq7YajcmKfdICjAMG0bb6ZtZP0SqEsZ6fAnfsfJ9ty5SKcQKBQCAQCH4YfFmR0Vi6+37vQtfhkEud0+x9FQF10l5k5zE0lkxatj9F9lUPxtlAHq3ZTnbhA4oMsN6ep1B2Dvu6Sz19h2vR2vqSog5FgvteMq7Rxy88/zR1Rw6iNVoIezr50YCz+MUv5zJu0hQAtmzcQOnDRdQfa+XMdBV/GGckJ7WT67Y8iTbrorhqwKiqtEDwXUOtVvPQQw8pnjvrrLPijjteJjiKCIS/J0RvAlE1w/aWJjwJZPRDPjdlyx5BpdWROeU+edKr3l7GeRdcFFdiFNvf4qrdHZHob65Ha7TEqRe+9PzT2MbPp2FdRITLNmSmQiG686M3CPvdhFytqPQmQl43hrQMmjY8TDjgRa0z0rSxmLDfjcaSjkUdpL2zHd/OZYQGz6KtuYmheRbF9x6ap+HTLxzfwhUWCAQCgUDwfaOnSJblgtF4D9XAnlWKNU7SNrS0bHJuX50wceD5RNl/G80AR/p3R8jn0Kb1RQoF5ZY0TSjArXfdJ6/r/rSihCeW/A8lxfei0hmwWlO59Y4F8tpv0IiJ/DhHuT6Ksr2inOcW/4bnJsDQPKvcQlY82sjLBWoKX/1frps5gx1vdqtKP1S8gOk9+oIFgu8jIhD+HpFI+r+njL4uM5dwMEDmlYn936LKiNHssjXdjrOqFEP+aFy11dhjpPx7mqdHd0t1mTlIwYDihuGq3Y3z7RfJvupBhVG8NxBC0urIHHcHWqudhrVF2CfehXbvCm65MMymf6k54GjEuuMJ9AZDQmGss39k/9ausUAg+OHQEvzy7/mxzQ1A5z8+BYWHsEAg+CaIJgAauzKnN9x425fKDp+ISBYk7kV2VC0nfXikJzFaYhwbKCcTvQo013cHvhVLCXk7qXt8WneQO7c7CC5b9gghtUZePzn3rqHj/a2UFN/LqqeXMee2uxg0Ivn3fekPj/HnCSS0mfzgNjPt3hBLljx4wtdLIPg+IQLh7xGJpP9jZfSbq5ZjGz4bx9aSXtUIE5U8L39iEfYp9yUMnqPHRndL0wbPoGXnM4pSZ+fbL2GfMF9pp1RwD82vlSGFgzjfeoGcO59Da+tLYO+z3HZhmJc/DLC6wCSLYM0s91D4ipryq+Fwe5jf7/bxWatETqaWij1vMXno8G/6EgsEgh8ALUGwWE2cbTd+qfelp3SSHTqKd2MlQW1WUg9hgUBwcohNAOQm2aQ/HscTyYpy4KO/43O3415bhEpvhFAI639MkzO7aYNnxLV4qQ0piYVDjRbqSqahNqRguXgStiEzZZGvW2NKoF96/mnCOhP2ruSFq3Y37tpqsqcVKZISs0Lw49kzEn6/z+qOJKymO+AIJ/QIFgh+SAixrNOM6l3bmDO7gIIxg5gzu4DqXdvk1xqPfJ5057Fl50psw2djzh8hlzvH0pv/28gxEwl5XfK5XbW7Obr6ThrWPUBjwxfyGKKiDRpzOumjb0EKBmjc8BB1j0+TzeV7ji3Y1oB9wnzC3kh/TNqQmbQ7nWz6V5DVBSZGDdSi06gYNVDLmkITobDEpFckFu7w8afJJnwPWHl+YpDlLz1DxZ63vta1FQgEgmgQ/POsg5xj+ORL/ZcdOoru4Mf4VSIIFgi+DWITAFHxKWvXJv2JkkhwKlaYqnrXNq6ZfBmv7XwN+9Qi8u7eSHbhb1Ebzbje3yq/T2NORxMK0L5tCfXLZ9KwtgijwUhrRbzo1bxfP0Cf/gPJmlpE+vBZScfeeORzgm0N8vqpbd86dNkDadq0mLrHp9G0aTFS5gDWrFqW9PudmdefPXUhxXN76kIMtKm4pQrm3zM/yTsFgu8/IiN8GnG8nc9ku5pqYwoZY2/HkJOP91AN6oAHZ1UptvHdu5bH83+LnjvkasX51gtk9lIi/dLzT9PSVaLk9Xowjp1Ly86VSST+c7rM5T20LJ9KpzeI1QAHHGGG5mkUYxiap6HTL5GaZmBNgUpR5vPnSUHueHWNyAoLBIKvTEsQfpwrkas7iO3IvwkFDV/6HM3/bP8GRiYQCBJxomXNvZFIcDQqTBVdd3mDIbKnKQVI7ZMX4thUjGf7Mho7OpACXkzWNMJqHdlXdVssOatKI8e0OkjN7IOWEE8svg+0BjI6lBonPcee3f8Mmjs98vop4Kgn5OlQ2C81VSzF43KyfetmWRwrlht+9RtuXvwb/jyh22Zy5gYPAY2RZcuXMnHi2BO+VgLB9w0RCJ9GJCp9ji1PTuald+UVBeyPUR6ce9d9SVWjo/TsufnZzy6lensZ/jBkjp8XN4ZVTy+TS6p7nmfFimWYzhku+wLH+u/Zhs+mafOj9LGoWFOoZ2ieid/v9vHHd/0J+4HNOol2p5OheVbFtYmIZjVhOPy+4nlJb8Dg9x332qo1AZx+aAn4AdWX/m0EAsH3gzOzTZhdEAoaCOj7fIUzNAGgOfYZ2qAz6VEnOjcBhLDxWaMHSPkK4xEIvr+caFnz8UimOh1dd7nXFiWsagt53QRTUsm+6rcYcvI5uuqOOA0W2/j5sGcVQ4eN4fWdr2GfvBBbtMe4shSVWqMQzood+w033kbZskdwbCvBfMFYVHoTWT3Et7ImL6SxfBHPPFmWMBAeN7mQ/6vZT+Ha52n3SZxnVzPnEh1/+ZdY6wgEIhA+jTjezmdvu5qxVO/aRvXb1QlVo2N3QGMzz9Xbyxg5bCSVm9cmvBk0OI7FqUjHjumJJb9DCnhp3FiM5HOjtfUlbej1kVKiundYM8MkB72LRhv5Z2Nkx3LNVd09wjduCfD4vQtZ/uzz7KnrjAuSzzmjD2nTlb7BJlMKHo/7uNfW6Yf6QB/+Xa8iQ/y/QiAQfEXMF18AgM7fgEabCgN/nPC4E52bAmioddk55kxB7/Gc1LEKBKc7yRIAvVW4fRmi6y610Zww4FbpTYoERWwZcxRDTj51Rw7ymqOBrKlFyqzyhPk4tpaQcs6QhGOPrqGeWlZM5z9eR/J7Ep5f8ns4VvdZ0u/x7ls7Kb/GpFg3jRkYZO5Dj4iMsOAHjVjyn0acyM7niXjpJcose84ZzvInFvHE4vvQGM2kXDQhLuu7f88q+uQMTCz8YOurEM6KJdafOORqpfWtFwg6j9Gy/SlUgBSQ4sqg11+dgmFRB3OrvBxwhBloUxFWWyi8fBQAN/5hGc8XIAfJsyuC3PXwQo78//buPS6qct8f+IeZYZgFjHjB2xZI86RmHY/aPuY+amqIF0gzjTC3F1LbamzzQnihMrdbkDISwyCOFhszd4ip21sdxW118LQ7v/xl+7g9Ul4RLwmaMDMsmOv5A5kYmOEiA2sW83m/XvuVrDXzrO9ae17fWd951vM8ylCHdtRQw6g0Nnptz5aooNeJLIKJqMU01iJYVApc+6exLl/T1Nx0p9x47wc6FsFEdTW1A6ApnM0+XXPfFTBwDEoObkLXWmvtlh7cVK8wdTZzdM3kWOZKvfNeZf0dFL39TL11i2ufY809VMn+ZOcFuZ8/evTo6fLcfrx8DSNnOpkwa+e1Zl8novaEt/0y0pRfPpuyjEDdnmXD2S+rl0aq1UPsbMH3q9cuY0XiRmzevBFdp6x0eMQ5aORvceuI68kaah7vsSh9HWZULPt8M9SVJqePQQ/sqsCZl6oTt8ligyZJDwBQmMtRYtLgmQNKlJeVoUNQEESTL34stuC//2FyOK4gqCCKjtucM7EIJqIW01iLYOrTH+cMwbhZVP0DmzNNz018SoWcO3bsGD7//HOkpqZKHYqkmtIB0BhXc7CMGTUGXxxNh3b8EtisVvtTbUqNPyZMmIJTp75xKEydzRytO5oOs6iHb5dQlx0JAGAw6PFO8hrszMmqd+9Wc9/mrCAvObgJCgC/i1vi8vwe6t0LBUV36t1nPfxgrxZdt7bU0mWyiJzh16uMNPbLZ1OXEajbs1z2dS6C64z7dbbge7devTEmPArbMlNx+/N0mMt+gm+XEHR8Yg6UAZ0aHJMzJjwK27PSoBlXZ4zzxOUwHEzG83sr8OdpGnsPb+xfRKSE/7J0SUGRBf1DugIANuw4jKDJ1Us51azSqbnyd+zfmYWnxjsmRa1GBV1T7jWJiNzEGBCKm9d8oBZdP2XC3EQtsWHDBhQUFODhhx+WOpR2wdUcLKcKtmPx4mXYmZMFg5MC7Ivjhx06B5QBnWAzGVF6JA2W8hJ0D+ljf78Y+q8oOfCW046E20fSANgQFr8PVcVnkZ62Eduz0lB++yd069UbHbpUr/bROWIRAKBkfzKslQb4qDXQ+PpizvK1TscH11gUn4h5617Bh5N+mTBr3mfA+tQ1rX5t3cEdy2QROcNCWGYa+uWzscm0atTtWTbdbnzB99o9zy8ujkfm+2noErOhWWNyym//hCAnx7lVUYHnnpmEaXn/gTLRDK1GAV+FAj0CfWCy2KoT9mErVsfNBQBcvXatxbNEEhERydXQoUMxbtw45ObmSh1Ku+BqDpai4kt4J3kNuvXqjRWJG50+tly3c6DzuN9BGdAJKNiO7TsO2F9bU7g560jwUanho1ShovAkAMCi9IVm3BIE3bvHEg++hcp7q310enIB/Pv9m/2+a0x4FO6Yq4/h4+tb79w6+esxY8Z4+Kk2IC7lTRReuYX+D3TD66/Pw/OThkA0XG3ydTIG9G7ya92pqfe3RM3FQrgdaeoyAnV7llWaQJdjWq6mTqvX8zwmPAoajQbbs7Y0a0yOqzHOISGhSFkeh5Tlcfbte4+dQFx2DgqLS9A/pCtWx821jw8O7dXLLbNEEhERebK8vDzk5OQ4bEtOTkZkZCS++eabJrejUCig1Wobf6HElEqlJHF2D33Q5WPLv1qQWd0D+X4aNBoNIiZOdXjvkuWvYnNaimPnwLF0LF+22n4uk6fOgEajwda0jdCJIrrXem3pkc3oPP4lqLTBuP3ZFtgABEcudyj6Ok1eicpj78Lnvz7E1asX0T30QSxfvhoRE6fiVqUZ335xAK/u3Iprly8hrHcY1r4yFzHTxttj9LP64MVpw/HitH2w/Fhk3267UAy11dqka+TjI0LRR4AgPAxBqIRW03YlREP3t3L4XAPSfbapYSyE25HmLCNQu2e55pGTumOPl654zWVxGzFxKoaPCG9WfLPmLkRGneMYjm7But/PqffaaRFj7YVvXa/NicKKrHeBiJdbZZZIIiIiTxAdHY3o6OgWt2O1WqHT6dwQUevSarWSxPnb2S/Wuw8qPbIZnUbHwkepqr6viliC7Vlb6t37DB8RjuWAY+fAomUYPiLc4VyGjwjH8BHh+OL4YWzPSsNPpTeh8A+Cj68fbh9Jg2+XEPgPHIPyr3c7fUrv1p1b2JmX77Bdp9Ph+NHD+GhbGrQRSxA6bSAqi89i6WtpsBTfRHT4cABA3VEYNUvDaQO00JU37Xr7GkUojSJ6BNyB2EPAj5d+brP5Cxq6v5XD5xqQ7rPdmiw2G25XWtzebl+3t+gaC+F25H6XEXDnrIuA6wkNxoRHQW8BDnyyDVeLLiK0V0+s+/0cTK9V8O49dgJbavUEL31hbr2CODp8OBQ9OuONzR/gau6lFsdLRERE8uOuCZTq3gdB5YfO4xfb50kBnD9hV/f4zh6fdvXaMgA+vn4InrQUfiEDUXZyF6zf7YEPbCjbHgvfUfMRMLD6/sdVp8YdM7Dv43+HNsLxseHK/mOQsHk7Fm3MwkPdOuGlCRMx5ddDar2zer1zq78AsaJpM9KrzHcRpKoCHq0fR2tr7WWy6D7ZAKXF/YVwW2Ih3I60pKBt6ayLNQn+p2uXoPDzR+CQpxA64x2HCQ0AYO/OLJQWX0KQvxLFxdfwbnYOfFDdA7z32AmkvJeOD6MUGBkWiIIiPea9lw7c21/bc1PDMWJ6LP77H5ztmYiIyNu05gRKCqUCP5/40N5TG/SbmHqTgjbn+M5eqzj4FgIGjoHmgUEwnD0B4X8PYNdzfvcmszJi5l/SYbBaodR2bbDoKym+VGclkHttRde0VYV5h/bB2rErJo98wvE8O2hhaWKPMG5ehMXsB7XhKoB+TXuPm7i7w4aoBkuIdqbuI887c7LsEz201lTztRN8WK2ZEO3LL41fgm2ZqTBDCd/+o9Bdfx27pigwMkxwKHa3ZOfgtwOtWPKZEf9basXDwQr8dqAKW7JzXD4mTURE5I0ef/xxPP7441KHIRl3TqDkrFAtPbIZXSKXQaUNRumRzVBaTFiy7JdZlmsf33D2S5R9nQvTzzex5Z0NDq+5de0ylJoAdHl6jUOsXSevRMn+ZJT/LQ9aPwV2P6e2L280to8Ku54GpnyyGQHdHmyw6OvxgOP4ZvPXH2H3FIVDWx8+ZcbiPX+uVwjLSc39bXt8xJiko5A6AGod9nG/IxcgNH4vMHIBMjPT8MXxw24/Vu0vg5qxNF0mLUXZ19WzWfqFDIROp4N2/BL4/PgFdt1L0L5Kn+oEHaVt+9AtAAAWo0lEQVTAluwcnLt6C7v+x4T0SRpUvqpF+iQNdv2PCeeu3nJ7zERERCRft65ddj6W9j5WkHB2HxMcuRzl3+yx/1sQhHpr+/qFDITh7Je4+9UOdB63CGHx+9Dl6TXYuvVtpKdttN+DmSv1TmO1VhkQFr8X+qrqZY1qGxmmRIXJB9t3HGiwsP/dy8tgOPYuKq/8HTaLGWW3bzlt68cbpc2+LkTtHQvhdspZUtfe+6XU3Vx9GZluFwOoHttiM1VWj4G5XeI0QRcWl6CjoMQHUwSHIvmDKQI6Co6vJyIiIu9WM4FSbfe7gkRj9zF+IQNRfvsnp8cv+zoXXSYtdbjf6jhpOay+gn2bb5dQp7H6dgmFj1KFoC7dUFDkONayoMiC3iE9G419wuRnkPrGfPgVZOBq6jPooFE5beuhnsFNvh5E3oKFcDvlzl9KG+Pqy8i3cwgqr/wduqPpCArugariswjq0tVpgu4f0hVllRanRXJZK8xIR0RERPI1a+5C6I6m23tCa+43Zs1d2Oy2XN7HdAmx/7tugV1zfNPtq07vt8xlP8Fw9ktc/+AlmG5fxa19Sfj5q4/ssZZ+tgVBv4kBAKh+MxszD1hx4pIZJosNJy6ZEXvYhmlzXm5S/M9OHYfvc/6Ii1vexLoXX8K8Q45tzTtkxaJnn2/2dSFq7zhGuJ1qzlJKLeVsNr/Sg5tgMdwFCrbbJ3jIzEyDb/8xmHngAHZNMd+bxMGCeYetWB03F1uyc1BQpLePawFqiuRubo+ZiIiI5MudEyg5vY85shkdR822F9h1J6uqOc6WdzY4vd9SCB1w96sd6HJvVuiq4rMoOfgWyr/eDZUQiIDBkfZZqQMGjsXd0qt4Jm8vdJXVPcHPLXoZo8Ofava51IwDXrznz/jxRike6hmMpbOel/X4YKLWwkK4nWrLqeadfRktW7ba6ZfRjpws/HS3CtP2KFEumtE/pBtWx/2yRNK899LxYVT9IpmIiIiotpaueFG7HeCX+5gOXbojQOWDO0fSGiywa7Zlvp8GRPxyv3X3s82AxYguU1bWmyDr9l82wizqYfj7UQgP/Iv9PabC/8TiFUluOZ/JI59g4UvUBCyE26m2nmq+KV9GY8KjMGh0FB7q0wl9OosIOJOPSkWYfX9NMRxXax3h2kUyERERUWu436J6THgUNBoNtmdtsd9v/f73ryA1ebXzR6Yr9Qh7ZR/unvwzSvYnwVpVge69+nA5ICIJsBBux9z1S2lbmhYxloUvERERyUbExKkYPiLcYdvOnCynj0zXTJDV6YnZEB74F6BgO7bvONDWIRMROFkWEREREZFbOZvMq/YEWYDrSUzvmJ3/7/DRQ1gyZzyeDv9nLJkzHt/911F08tdDcelH6L//sQ3Pjqh9YI8wEREREZEb1R2iptQEOEyQBTifxPSOGQjUCugZrHHYfvTgXny2fT2yI4GRYYEoKLqLeVtfwyOWf2Bip+4wqzrC0uPBVj8vovaEhTARERERkZvVHqL2xfHDyMxMQ2WtCbLqTmJaUwQPDDOjo0Z0aOuT91KQHQn7yhpj+6jwYaQZcX/aj3EJq1gEE90HSQthnU6HhIQE6PV6mEwmrF69GkOGDJEyJLoPXxw/jJ05Wbh1b5KIWXMXym5sMlFtzE1EROROjU1iWrsI7nH+SyhhdXj/DxevYeTzWodtI8OUKCy+3TYnQNQOSVoIZ2dnY/jw4YiNjcXFixcRHx+Pffv2SRkSNVPNL5za8UsQeu8XzszMNABgMUyyxdxERETu5moS09pFcDfLdcDsi0p1d4fX9A/phoIivb1HGAAKiix4qFsnAIDy5kUAaNWeYaWqClYAN0orW+0YRG1J0kI4NjYWarUaAGCxWODn5ydlOHQfduZkQTt+icM6eRi/BDtzslgIk2wxNxERUVvqGaxBR40IX4MFFif7l74wF/PeS8eHUWaMDFOioMiCeYetWB23AAFDHgUAVP2/AqiK/3+Tj2lT+8HPWNWk1yqUJljQEecMwQCAzhxcSRKwWq1Yt24dCgsLoVarsWHDBjzwwAP2/YcOHUJOTg6USiX69euHdevWQaFwPTd0m32M8/LykJOT47AtOTkZgwYNQklJCRISEpCYmNhoOwqFAlqtttHX1aVUKu/rfW1NbnHeunYZoU7Wybt67bLT8xArzRAEAYIA+Kn94Ovf/HP1MZRDKfhDDTUEQQWtxvXHWA7XUw4xAvKJs7mkzk2AfK6tHOJsSYzV+UkDweoPiyBAE9D8dhQVfrAKAtR+VmhV8s5NAOMk7yCHIV41S0vGZeegsLgE/UO6YnXcXPt2jbUIvo/1hrXPQ01u00+thspobPLrzxqCcfOuP9Si2PiLiVpBfn4+jEYjcnNzcfr0aaSkpCAzMxMAUFlZibS0NBw8eBCCIGDFihU4ceIEwsPDXbbXZoVwdHQ0oqOj620vLCzEihUrsHLlSgwbNqzRdqxWK3Q6XbOPr9Vq7+t9bU1ucXbr1dvpOnndevV2eh6iGRBFDURRhMJYhUpz88/V1yhCKVbAqDRCFE3QmRqP05PJIUZAPnE2l9S5CZDPtZVDnC2JsTo/2aAQK2ARRZgszW9HY62CKIowVvlA18DNohyuJcA4qf2T0xCvaRFj7YVvDV/jT1CqqmDq0x+3lL/CzxWBTW5PUAgQq5pW1N4orYReJ6KzikUwVTNarCi64/7H5B9rYN+pU6cwatQoAMDgwYNx5swZ+z61Wo1PPvkEgiAAAMxmc6NP9En6YMP58+exdOlSpKWlYcCAAVKGQvdp1tyF1V8Y45e4nAWRSG6Ym4iIvIOch3jVLoLPGYJx864Ket3PTX6/IFRCbEbvLh+HptqUADoo3d/unTt3sGDBAvvfMTExiImpXn9br9cjMPCXH3uUSiXMZjNUKhUUCgWCg6sf3f/oo49QUVGBESNGNHgsST/SqampMBqNSEpKAgAEBgbau7dJHhqbBZFIjpibiIi8Q0NDvGShzz/BGBCKm9d8oBbFZhWrWo2qwafqiKTQuXNn7N271+m+wMBAGAwG+99WqxWqWsOQrFYrNm3ahEuXLiE9PR0+Pj4NHkvSQpg3lu2Dq1kQieSKuYmIyDs0NMSLiDzL0KFDceLECURGRuL06dPo16+fw/61a9dCrVYjIyOjwUmyavAhByIiIiLyShziRSQfEREROHnyJGbMmAGbzYbk5GQcPHgQFRUVePTRR7Fnzx78+te/xty5cwEAc+bMQUREhMv2WAgTERERkVfiEC8i+VAoFFi/fr3Dtr59+9r/fe7cuWa1x0KYWt2Xxw9h7453cbn4BsJ69UTsijVYOHeS1GERERERcYgXkZdiIUyt6svjh7D7/T/gT1E+GBkWiIKiu3gheRW6BZgwa4C/1OEREREREZEXanwUMVEL7N3xLv4U5YOxfVTwVVb/NzsSePetVKlDIyIiIiIiL8VCmFrV5eIbGBnmuMjYyDAlfrh0XaKIiIiIiIjI27EQplbVO6QnCoosDtsKiizo1+dXEkVERERERETejoUwtappc15G7GEbTlwyw2Sp/u8LR4CXV8ZLHRoREREREXkpTpZFrWp0+FMAgAW1Zo1+IXENnnl2EnAmX+LoiIiIiIjIG7EQplY3Ovwpe0F8xww81KcTAFHaoIiIiIiIyGvx0WgiIiIiIiLyKiyEiYiIiIiIyKuwECYiIiIiIiKvwkKYiIiIiIiIvAonyyIiIiIiIqImqzJacP5yudvbDX/M7U26xEKYiIiIiIiImsxX4YNfaX2lDqNF+Gg0EREREREReRUWwkRERERERORV+Gg0ERERkUzodDokJCRAr9fDZDJh9erVGDJkiNRhERHJDgthIiIiIpnIzs7G8OHDERsbi4sXLyI+Ph779u2TOiwiItlhIUxEREQkE7GxsVCr1QAAi8UCPz8/iSMiIpInFsJEREREHigvLw85OTkO25KTkzFo0CCUlJQgISEBiYmJjbajUCig1WpbK0y3USqVsoizrpbGLVaaIQgCBAEQrP6wCAI0AY2352Moh1Lwh1UQoPazQqtq3m29HK+3HGMG5Bt3e8dCmIiIiMgDRUdHIzo6ut72wsJCrFixAitXrsSwYcMabcdqtUKn07VGiG6l1WplEWddLY1bNAOiqIEoilCIFbCIIkyWxtvzNYpQihUQFSKMVT7QiWKzjivH6y3HmAH5xt3esRAmIiIikonz589j6dKlSEtLw4ABA6QOh4hItlgIExEREclEamoqjEYjkpKSAACBgYHIzMyUOCoiIvlhIUxEREQkEyx6iYjcQyF1AERERERERERtiYUwEREREREReRUWwkRERERERORVJB0jXFFRgfj4eJSVlUEQBGzatAmdO3eWMiQiIuYmIiIionZO0h7h3bt345FHHsGuXbsQFRWFjIwMKcMhIgLA3ERERETkaaxWK9auXYuYmBjMnj0bV65ccdj/17/+FdOnT0dMTAx2797daHuS9gjHxsbCYrEAAK5fv47g4GApwyEiAsDcRERERORp8vPzYTQakZubi9OnTyMlJcU+k77JZMLGjRuxZ88eCIKA559/HmPHjkXXrl1dttdmhXBeXh5ycnIctiUnJ2PQoEGYM2cOfvjhB2RnZ7dVOEREAJibiIiIiOTg1KlTGDVqFABg8ODBOHPmjH3fhQsXEBYWhqCgIADAY489hm+//RaTJk1y2V6bFcLR0dGIjo52um/Hjh24cOECFi5ciPz8/AbbUSgU0Gq1zT6+Uqm8r/e1tfYep1hphiAIEATAT+0HX//mt+FjKIdS8IcaagiCClqN64+xHK6nHGIE5BNnc0mdmwD5XFs5xNmSGKvzkwaC1R8WQYAmoPntKCr8YBUEqP2s0KrknZsAxklERM51DPLH5PH/7PZ2b9y4gbi4OPvfMTExiImJAQDo9XoEBgba9ymVSpjNZqhUKuj1eofvgYCAAOj1+gaPJemj0VlZWejevTumTp0Kf39/KJXKRt9jtVqh0+mafSytVntf72tr7T1O0QyIogaiKEJhrEKluflt+BpFKMUKGJVGiKIJOpP742xLcogRkE+c7tCWuQmQz7WVQ5wtibE6P9mgECtgEUWYLM1vR2OtgiiKMFb5QCeKrRJnW2KcRETUlnr27Im9e/c63RcYGAiDwWD/22q1QnXvR+e6+wwGQ6M/kEpaCE+fPh2rVq3Cp59+CovFguTkZCnDISICwNxERERE5GmGDh2KEydOIDIyEqdPn0a/fv3s+/r27YsrV67g7t278Pf3x7fffov58+c32J6khXBwcDA++OADKUMgIqqHuYmIiIjIs0RERODkyZOYMWMGbDYbkpOTcfDgQVRUVCAmJgarV6/G/PnzYbPZMH36dHTv3r3B9iQthImIiIiIiIgao1AosH79eodtffv2tf/7ySefxJNPPtn09twWGREREREREZEMsBAmIiIiIiIir8JCmIiIiIiIiLwKC2EiIiIiIiLyKj42m80mdRBEREREREREbYU9wkRERERERORVWAgTERERERGRV2EhTERERERERF6FhTARERERERF5FRbCRERERERE5FVYCBMREREREZFX8ZpCuKKiAosXL8bMmTMxf/583LlzR+qQnNLpdFi0aBFmzZqFmJgYfPfdd1KH1KBjx44hPj5e6jAcWK1WrF27FjExMZg9ezauXLkidUgN+v777zF79mypw3DJZDIhISEBM2fOxLPPPovjx49LHVK7I4f8xNzkHnLKT8xN1Nbklmfq8tS8U5ucclBdnp6T6mKO8nxeUwjv3r0bjzzyCHbt2oWoqChkZGRIHZJT2dnZGD58OHbu3ImNGzdi/fr1Uofk0oYNG5Camgqr1Sp1KA7y8/NhNBqRm5uL+Ph4pKSkSB2SS9u2bcNrr72GqqoqqUNx6cCBA+jYsSN27dqFbdu24Y9//KPUIbU7cshPzE3uIZf8xNxEUpBTnqnLk/NObXLJQXXJISfVxRzl+VRSB9BWYmNjYbFYAADXr19HcHCwxBE5FxsbC7VaDQCwWCzw8/OTOCLXhg4dinHjxiE3N1fqUBycOnUKo0aNAgAMHjwYZ86ckTgi18LCwpCeno6VK1dKHYpLEydOxIQJE+x/K5VKCaNpn+SQn5ib3EMu+Ym5iaQgpzxTlyfnndrkkoPqkkNOqos5yvO1y0I4Ly8POTk5DtuSk5MxaNAgzJkzBz/88AOys7Mliu4XDcVZUlKChIQEJCYmShTdL1zFGRkZiW+++UaiqFzT6/UIDAy0/61UKmE2m6FSed7HfcKECSguLpY6jAYFBAQAqL6uL7/8MpYtWyZxRPImh/zE3NR65JKfmJuotcklz9Qlx7xTm1xyUF1yyEl1MUd5Ps/+1N+n6OhoREdHO923Y8cOXLhwAQsXLkR+fn4bR+bIVZyFhYVYsWIFVq5ciWHDhkkQmaOGrqcnCgwMhMFgsP9ttVo9PsF7uhs3biAuLg4zZ87E5MmTpQ5H1uSQn5ibWg/zk3sxN8mXXPJMXXLMO7UxB7Ut5ijP5jVjhLOysrB//34AgL+/v8c+nnD+/HksXboUqampGD16tNThyNLQoUPx1VdfAQBOnz6Nfv36SRyRvJWWlmLevHlISEjAs88+K3U47ZIc8hNzk3swP7kPc1P7wzzT+piD2g5zlOfzmp+Apk+fjlWrVuHTTz+FxWJBcnKy1CE5lZqaCqPRiKSkJADVv9xlZmZKHJW8RERE4OTJk5gxYwZsNpvH/n8tF++//z7Ky8uRkZFhn8Rp27Zt0Gg0EkfWfsghPzE3uQfzk/swN7U/zDOtjzmo7TBHeT4fm81mkzoIIiIiIiIiorbiNY9GExEREREREQEshImIiIiIiMjLsBAmIiIiIiIir8JCmIiIiIiIiLwKC2EiIiIiIiLyKiyESRLnzp3Do48+io8//thhu9lsxrRp0/DKK684bM/IyKi3jYioNTA/EZEnYm4ici8WwiSJAQMGYMGCBUhNTcX169ft27du3YqSkhK8/vrr9m2HDh3C1q1bpQiTiLwQ8xMReSLmJiL3YiFMknnppZfQs2dPrF27FgDw/fffY9u2bUhKSkJQUBDMZjPeeOMNJCYmIjQ0VOJoicibMD8RkSdibiJyHxbCJBm1Wo2kpCScPHkS+/fvR2JiIqZPn44nnngCAFBRUYGLFy9i9+7dGDJkiMTREpE3YX4iIk/E3ETkPiqpAyDvNnjwYMyaNQuvvvoqevTogVWrVtn3dejQAR999JGE0RGRN2N+IiJPxNxE5B7sESbJjR07FmazGQMHDkRAQIDU4RAR2TE/EZEnYm4iajkWwiQpURSxbt06DBs2DEePHkV+fr7UIRERAWB+IiLPxNxE5B4shElSb7/9NiorK5GRkYGnn34a69atQ3l5udRhERExPxGRR2JuInIPFsIkmb/97W/4+OOP8Yc//AFarRZr1qyBxWJBSkqK1KERkZdjfiIiT8TcROQ+LIRJEnq9HomJiXjqqacwduxYAECnTp2wZs0afPrppzh58qTEERKRt2J+IiJPxNxE5F4shEkSb775JiorK/Hqq686bJ8yZQpGjx6N119/HQaDQaLoiMibMT8RkSdibiJyLx+bzWaTOggiIiIiIiKitsIeYSIiIiIiIvIqLISJiIiIiIjIq7AQJiIiIiIiIq/CQpiIiIiIiIi8CgthIiIiIiIi8ioshImIiIiIiMirsBAmIiIiIiIir8JCmIiIiIiIiLwKC2EiIiIiIiLyKv8HeIRd1glPpr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now set the class weights so that false positives have a weight equal to the imbalance ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "IR=0.05/0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weight={0:IR,1:1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "classifier = sklearn.tree.DecisionTreeClassifier(max_depth=5,class_weight=class_weight,random_state=0)\n",
    "\n",
    "(results_df_dt_cost_sensitive, classifier_0, train_df, test_df) = kfold_cv_with_classifier(classifier, \n",
    "                                                                         X, y, \n",
    "                                                                         n_splits=5,\n",
    "                                                                         strategy_name=\"Decision tree - Cost-sensitive\")\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree - Baseline</th>\n",
       "      <td>0.007+/-0.003</td>\n",
       "      <td>0.006+/-0.002</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - Cost-sensitive</th>\n",
       "      <td>0.007+/-0.002</td>\n",
       "      <td>0.006+/-0.001</td>\n",
       "      <td>0.887+/-0.034</td>\n",
       "      <td>0.471+/-0.059</td>\n",
       "      <td>0.898+/-0.021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Fit time (s) Score time (s)        AUC ROC  \\\n",
       "Decision tree - Baseline        0.007+/-0.003  0.006+/-0.002  0.906+/-0.025   \n",
       "Decision tree - Cost-sensitive  0.007+/-0.002  0.006+/-0.001  0.887+/-0.034   \n",
       "\n",
       "                               Average Precision Balanced accuracy  \n",
       "Decision tree - Baseline           0.528+/-0.072     0.786+/-0.046  \n",
       "Decision tree - Cost-sensitive     0.471+/-0.059     0.898+/-0.021  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([results_df_dt_baseline, \n",
    "           results_df_dt_cost_sensitive])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the decision boundary was shifted towards samples from the minority class. This shift allowed to increase the performance in terms of balanced accuracy, which increased from 0.786+/-0.046 to 0.898+/-0.021. We note however that the performances in terms of AUC ROC and Average Precision both decreased. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now apply the same methodology with a logistic regression classifier. We first build a classifier with equal weights for the two classes and run a stratified 5-fold cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "classifier = sklearn.linear_model.LogisticRegression(C=1,class_weight={0:1,1:1},random_state=0)\n",
    "\n",
    "(results_df_lr_baseline, classifier_0, train_df, test_df) = kfold_cv_with_classifier(classifier, \n",
    "                                                                          X, y, \n",
    "                                                                          n_splits=5,\n",
    "                                                                          strategy_name=\"Logistic regression - Baseline\")\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic regression - Baseline</th>\n",
       "      <td>0.01+/-0.002</td>\n",
       "      <td>0.005+/-0.002</td>\n",
       "      <td>0.937+/-0.012</td>\n",
       "      <td>0.535+/-0.065</td>\n",
       "      <td>0.641+/-0.048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Fit time (s) Score time (s)        AUC ROC  \\\n",
       "Logistic regression - Baseline  0.01+/-0.002  0.005+/-0.002  0.937+/-0.012   \n",
       "\n",
       "                               Average Precision Balanced accuracy  \n",
       "Logistic regression - Baseline     0.535+/-0.065     0.641+/-0.048  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df_lr_baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performances in terms of AUC ROC and Average Precision are higher than with a decision tree, but lower in terms of balanced accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision boundary illustrates the linear separation that results from logistic regression. Due to the class imbalance, we observe that the decision boundary slightly favors the majority class. \n",
    "\n",
    "As for the decision tree, let us change the class weights, using the imbalance ratio as the weight for the majority class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "classifier = sklearn.linear_model.LogisticRegression(C=1,class_weight={0:IR,1:1},random_state=0)\n",
    "\n",
    "(results_df_lr_cost_sensitive, classifier_0, train_df, test_df) = kfold_cv_with_classifier(classifier, \n",
    "                                                                         X, y, \n",
    "                                                                         n_splits=5,\n",
    "                                                                         strategy_name=\"Logistic regression - Cost-sensitive\")\n",
    "\n",
    "fig_decision_boundary = plot_decision_boundary(classifier_0, train_df, test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic regression - Baseline</th>\n",
       "      <td>0.01+/-0.002</td>\n",
       "      <td>0.005+/-0.002</td>\n",
       "      <td>0.937+/-0.012</td>\n",
       "      <td>0.535+/-0.065</td>\n",
       "      <td>0.641+/-0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic regression - Cost-sensitive</th>\n",
       "      <td>0.008+/-0.002</td>\n",
       "      <td>0.004+/-0.001</td>\n",
       "      <td>0.937+/-0.012</td>\n",
       "      <td>0.536+/-0.064</td>\n",
       "      <td>0.899+/-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Fit time (s) Score time (s)  \\\n",
       "Logistic regression - Baseline         0.01+/-0.002  0.005+/-0.002   \n",
       "Logistic regression - Cost-sensitive  0.008+/-0.002  0.004+/-0.001   \n",
       "\n",
       "                                            AUC ROC Average Precision  \\\n",
       "Logistic regression - Baseline        0.937+/-0.012     0.535+/-0.065   \n",
       "Logistic regression - Cost-sensitive  0.937+/-0.012     0.536+/-0.064   \n",
       "\n",
       "                                     Balanced accuracy  \n",
       "Logistic regression - Baseline           0.641+/-0.048  \n",
       "Logistic regression - Cost-sensitive      0.899+/-0.01  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([results_df_lr_baseline, results_df_lr_cost_sensitive])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig_decision_boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the decision boundary moved to the left, favoring the classification of the minority class. We note a strong increase of the balanced accuracy, from 0.641+/-0.048 to 0.899+/-0.01. The AUC ROC and Average Precision remain as good as the classifier with equal weights. \n",
    "\n",
    "The examples above show that tuning the class weights can improve classification performances. It is however worth noting that the performance improvements depend on the performance metric. For both classifiers, reducing the class weight of the majority class allowed to increase the balanced accuracy. The accuracy in terms of AUC ROC and Average Precision however remained unchanged for logistic regression and decreased for decision trees. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fit time (s)</th>\n",
       "      <th>Score time (s)</th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average Precision</th>\n",
       "      <th>Balanced accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision tree - Baseline</th>\n",
       "      <td>0.007+/-0.003</td>\n",
       "      <td>0.006+/-0.002</td>\n",
       "      <td>0.906+/-0.025</td>\n",
       "      <td>0.528+/-0.072</td>\n",
       "      <td>0.786+/-0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision tree - Cost-sensitive</th>\n",
       "      <td>0.007+/-0.002</td>\n",
       "      <td>0.006+/-0.001</td>\n",
       "      <td>0.887+/-0.034</td>\n",
       "      <td>0.471+/-0.059</td>\n",
       "      <td>0.898+/-0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic regression - Baseline</th>\n",
       "      <td>0.01+/-0.002</td>\n",
       "      <td>0.005+/-0.002</td>\n",
       "      <td>0.937+/-0.012</td>\n",
       "      <td>0.535+/-0.065</td>\n",
       "      <td>0.641+/-0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic regression - Cost-sensitive</th>\n",
       "      <td>0.008+/-0.002</td>\n",
       "      <td>0.004+/-0.001</td>\n",
       "      <td>0.937+/-0.012</td>\n",
       "      <td>0.536+/-0.064</td>\n",
       "      <td>0.899+/-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Fit time (s) Score time (s)  \\\n",
       "Decision tree - Baseline              0.007+/-0.003  0.006+/-0.002   \n",
       "Decision tree - Cost-sensitive        0.007+/-0.002  0.006+/-0.001   \n",
       "Logistic regression - Baseline         0.01+/-0.002  0.005+/-0.002   \n",
       "Logistic regression - Cost-sensitive  0.008+/-0.002  0.004+/-0.001   \n",
       "\n",
       "                                            AUC ROC Average Precision  \\\n",
       "Decision tree - Baseline              0.906+/-0.025     0.528+/-0.072   \n",
       "Decision tree - Cost-sensitive        0.887+/-0.034     0.471+/-0.059   \n",
       "Logistic regression - Baseline        0.937+/-0.012     0.535+/-0.065   \n",
       "Logistic regression - Cost-sensitive  0.937+/-0.012     0.536+/-0.064   \n",
       "\n",
       "                                     Balanced accuracy  \n",
       "Decision tree - Baseline                 0.786+/-0.046  \n",
       "Decision tree - Cost-sensitive           0.898+/-0.021  \n",
       "Logistic regression - Baseline           0.641+/-0.048  \n",
       "Logistic regression - Cost-sensitive      0.899+/-0.01  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.concat([results_df_dt_baseline,\n",
    "                        results_df_dt_cost_sensitive,\n",
    "                        results_df_lr_baseline,\n",
    "                        results_df_lr_cost_sensitive])\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Cost_Sensitive_Learning_Transaction_Data)=\n",
    "## Transaction data\n",
    "\n",
    "Let us now explore whether changing the class weights can improve the classification performances on the simulated dataset of transaction data. We reuse the methodology of [Chapter 5, Model Selection](Model_Selection), using prequential validation as the validation strategy. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data\n",
    "\n",
    "The loading of data and initialization of the parameters follow the same template as in [Chapter 5, Model Selection](Model_Selection)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load  files\n",
      "CPU times: user 724 ms, sys: 569 ms, total: 1.29 s\n",
      "Wall time: 1.41 s\n",
      "919767 transactions loaded, containing 8195 fraudulent transactions\n"
     ]
    }
   ],
   "source": [
    "# Load data from the 2018-07-11 to the 2018-09-14\n",
    "\n",
    "DIR_INPUT = 'simulated-data-transformed/data/' \n",
    "\n",
    "BEGIN_DATE = \"2018-06-11\"\n",
    "END_DATE = \"2018-09-14\"\n",
    "\n",
    "print(\"Load  files\")\n",
    "%time transactions_df = read_from_files(DIR_INPUT, BEGIN_DATE, END_DATE)\n",
    "print(\"{0} transactions loaded, containing {1} fraudulent transactions\".format(len(transactions_df),transactions_df.TX_FRAUD.sum()))\n",
    "\n",
    "\n",
    "# Number of folds for the prequential validation\n",
    "n_folds = 4\n",
    "\n",
    "# Set the starting day for the training period, and the deltas\n",
    "start_date_training = datetime.datetime.strptime(\"2018-07-25\", \"%Y-%m-%d\")\n",
    "delta_train = delta_delay = delta_test = delta_valid = delta_assessment = 7\n",
    "\n",
    "start_date_training_for_valid = start_date_training+datetime.timedelta(days=-(delta_delay+delta_valid))\n",
    "start_date_training_for_test = start_date_training+datetime.timedelta(days=(n_folds-1)*delta_test)\n",
    "\n",
    "output_feature = \"TX_FRAUD\"\n",
    "\n",
    "input_features = ['TX_AMOUNT','TX_DURING_WEEKEND', 'TX_DURING_NIGHT', 'CUSTOMER_ID_NB_TX_1DAY_WINDOW',\n",
    "       'CUSTOMER_ID_AVG_AMOUNT_1DAY_WINDOW', 'CUSTOMER_ID_NB_TX_7DAY_WINDOW',\n",
    "       'CUSTOMER_ID_AVG_AMOUNT_7DAY_WINDOW', 'CUSTOMER_ID_NB_TX_30DAY_WINDOW',\n",
    "       'CUSTOMER_ID_AVG_AMOUNT_30DAY_WINDOW', 'TERMINAL_ID_NB_TX_1DAY_WINDOW',\n",
    "       'TERMINAL_ID_RISK_1DAY_WINDOW', 'TERMINAL_ID_NB_TX_7DAY_WINDOW',\n",
    "       'TERMINAL_ID_RISK_7DAY_WINDOW', 'TERMINAL_ID_NB_TX_30DAY_WINDOW',\n",
    "       'TERMINAL_ID_RISK_30DAY_WINDOW']\n",
    "\n",
    "\n",
    "# Only keep columns that are needed as argument to the custom scoring function\n",
    "# (in order to reduce the serialization time of transaction dataset)\n",
    "transactions_df_scorer = transactions_df[['CUSTOMER_ID', 'TX_FRAUD','TX_TIME_DAYS']]\n",
    "\n",
    "card_precision_top_100 = sklearn.metrics.make_scorer(card_precision_top_k_custom, \n",
    "                                                     needs_proba=True, \n",
    "                                                     top_k=100, \n",
    "                                                     transactions_df=transactions_df_scorer)\n",
    "\n",
    "performance_metrics_list_grid = ['roc_auc', 'average_precision', 'card_precision@100']\n",
    "performance_metrics_list = ['AUC ROC', 'Average precision', 'Card Precision@100']\n",
    "\n",
    "scoring = {'roc_auc':'roc_auc',\n",
    "           'average_precision': 'average_precision',\n",
    "           'card_precision@100': card_precision_top_100,\n",
    "           }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree\n",
    "\n",
    "The transaction dataset contains around 0.7% of fraudulent transactions. The imbalance ratio is therefore around 1/100. In order to assess the impact of the class weight parameter on the classification performance, we vary the class weight in the range 0.01 to 1, with the following set of possible values: $[0.01, 0.05, 0.1, 0.5, 1]$. \n",
    "\n",
    "The implementation is the same as in [Chapter 5](Model_Selection_Decision_Tree). The only modification consists in varying the class weight parameter (`clf__class_weight`) instead of the decision tree depth. We use a decision tree depth of 5 (`clf__max_depth`). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define classifier\n",
    "classifier = sklearn.tree.DecisionTreeClassifier()\n",
    "\n",
    "# Set of parameters for which to assess model performances\n",
    "parameters = {'clf__max_depth':[5], 'clf__random_state':[0],\n",
    "              'clf__class_weight':[{0: w} for w in [0.01, 0.05, 0.1, 0.5, 1]]}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# Fit models and assess performances for all parameters\n",
    "performances_df = model_selection_wrapper(transactions_df, classifier, \n",
    "                                          input_features, output_feature,\n",
    "                                          parameters, scoring, \n",
    "                                          start_date_training_for_valid,\n",
    "                                          start_date_training_for_test,\n",
    "                                          n_folds=n_folds,\n",
    "                                          delta_train=delta_train, \n",
    "                                          delta_delay=delta_delay, \n",
    "                                          delta_assessment=delta_assessment,\n",
    "                                          performance_metrics_list_grid=performance_metrics_list_grid,\n",
    "                                          performance_metrics_list=performance_metrics_list,\n",
    "                                          n_jobs=1)\n",
    "\n",
    "execution_time_dt = time.time()-start_time\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us use the class weight as the varying parameter, and summarize the performances as a function of the class weight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select parameter of interest (class_weight)\n",
    "parameters_dict = dict(performances_df['Parameters'])\n",
    "performances_df['Parameters summary'] = [parameters_dict[i]['clf__class_weight'][0] for i in range(len(parameters_dict))]\n",
    "\n",
    "# Rename to performances_df_dt for model performance comparison at the end of this notebook\n",
    "performances_df_dt = performances_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC Test</th>\n",
       "      <th>AUC ROC Test Std</th>\n",
       "      <th>Average precision Test</th>\n",
       "      <th>Average precision Test Std</th>\n",
       "      <th>Card Precision@100 Test</th>\n",
       "      <th>Card Precision@100 Test Std</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Execution time</th>\n",
       "      <th>AUC ROC Validation</th>\n",
       "      <th>AUC ROC Validation Std</th>\n",
       "      <th>Average precision Validation</th>\n",
       "      <th>Average precision Validation Std</th>\n",
       "      <th>Card Precision@100 Validation</th>\n",
       "      <th>Card Precision@100 Validation Std</th>\n",
       "      <th>Parameters summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.825500</td>\n",
       "      <td>0.009464</td>\n",
       "      <td>0.537440</td>\n",
       "      <td>0.033668</td>\n",
       "      <td>0.289286</td>\n",
       "      <td>0.010903</td>\n",
       "      <td>{'clf__class_weight': {0: 0.01}, 'clf__max_dep...</td>\n",
       "      <td>0.555497</td>\n",
       "      <td>0.837454</td>\n",
       "      <td>0.014356</td>\n",
       "      <td>0.532924</td>\n",
       "      <td>0.025252</td>\n",
       "      <td>0.277500</td>\n",
       "      <td>0.010944</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.792912</td>\n",
       "      <td>0.029734</td>\n",
       "      <td>0.561153</td>\n",
       "      <td>0.046031</td>\n",
       "      <td>0.273571</td>\n",
       "      <td>0.019418</td>\n",
       "      <td>{'clf__class_weight': {0: 0.05}, 'clf__max_dep...</td>\n",
       "      <td>0.505740</td>\n",
       "      <td>0.808213</td>\n",
       "      <td>0.022140</td>\n",
       "      <td>0.565880</td>\n",
       "      <td>0.027737</td>\n",
       "      <td>0.266071</td>\n",
       "      <td>0.013716</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.784486</td>\n",
       "      <td>0.031698</td>\n",
       "      <td>0.556320</td>\n",
       "      <td>0.031025</td>\n",
       "      <td>0.272143</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>{'clf__class_weight': {0: 0.1}, 'clf__max_dept...</td>\n",
       "      <td>0.718678</td>\n",
       "      <td>0.814153</td>\n",
       "      <td>0.023492</td>\n",
       "      <td>0.572758</td>\n",
       "      <td>0.029783</td>\n",
       "      <td>0.269643</td>\n",
       "      <td>0.015791</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.798043</td>\n",
       "      <td>0.020988</td>\n",
       "      <td>0.579394</td>\n",
       "      <td>0.016007</td>\n",
       "      <td>0.278214</td>\n",
       "      <td>0.003093</td>\n",
       "      <td>{'clf__class_weight': {0: 0.5}, 'clf__max_dept...</td>\n",
       "      <td>0.622862</td>\n",
       "      <td>0.799682</td>\n",
       "      <td>0.013524</td>\n",
       "      <td>0.568408</td>\n",
       "      <td>0.018631</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.013420</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.810138</td>\n",
       "      <td>0.008586</td>\n",
       "      <td>0.600306</td>\n",
       "      <td>0.016797</td>\n",
       "      <td>0.284286</td>\n",
       "      <td>0.004286</td>\n",
       "      <td>{'clf__class_weight': {0: 1}, 'clf__max_depth'...</td>\n",
       "      <td>0.626287</td>\n",
       "      <td>0.804218</td>\n",
       "      <td>0.016505</td>\n",
       "      <td>0.546094</td>\n",
       "      <td>0.042197</td>\n",
       "      <td>0.267857</td>\n",
       "      <td>0.013869</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AUC ROC Test  AUC ROC Test Std  Average precision Test  \\\n",
       "0      0.825500          0.009464                0.537440   \n",
       "1      0.792912          0.029734                0.561153   \n",
       "2      0.784486          0.031698                0.556320   \n",
       "3      0.798043          0.020988                0.579394   \n",
       "4      0.810138          0.008586                0.600306   \n",
       "\n",
       "   Average precision Test Std  Card Precision@100 Test  \\\n",
       "0                    0.033668                 0.289286   \n",
       "1                    0.046031                 0.273571   \n",
       "2                    0.031025                 0.272143   \n",
       "3                    0.016007                 0.278214   \n",
       "4                    0.016797                 0.284286   \n",
       "\n",
       "   Card Precision@100 Test Std  \\\n",
       "0                     0.010903   \n",
       "1                     0.019418   \n",
       "2                     0.019548   \n",
       "3                     0.003093   \n",
       "4                     0.004286   \n",
       "\n",
       "                                          Parameters  Execution time  \\\n",
       "0  {'clf__class_weight': {0: 0.01}, 'clf__max_dep...        0.555497   \n",
       "1  {'clf__class_weight': {0: 0.05}, 'clf__max_dep...        0.505740   \n",
       "2  {'clf__class_weight': {0: 0.1}, 'clf__max_dept...        0.718678   \n",
       "3  {'clf__class_weight': {0: 0.5}, 'clf__max_dept...        0.622862   \n",
       "4  {'clf__class_weight': {0: 1}, 'clf__max_depth'...        0.626287   \n",
       "\n",
       "   AUC ROC Validation  AUC ROC Validation Std  Average precision Validation  \\\n",
       "0            0.837454                0.014356                      0.532924   \n",
       "1            0.808213                0.022140                      0.565880   \n",
       "2            0.814153                0.023492                      0.572758   \n",
       "3            0.799682                0.013524                      0.568408   \n",
       "4            0.804218                0.016505                      0.546094   \n",
       "\n",
       "   Average precision Validation Std  Card Precision@100 Validation  \\\n",
       "0                          0.025252                       0.277500   \n",
       "1                          0.027737                       0.266071   \n",
       "2                          0.029783                       0.269643   \n",
       "3                          0.018631                       0.265000   \n",
       "4                          0.042197                       0.267857   \n",
       "\n",
       "   Card Precision@100 Validation Std  Parameters summary  \n",
       "0                           0.010944                0.01  \n",
       "1                           0.013716                0.05  \n",
       "2                           0.015791                0.10  \n",
       "3                           0.013420                0.50  \n",
       "4                           0.013869                1.00  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performances_df_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.837+/-0.01</td>\n",
       "      <td>0.573+/-0.03</td>\n",
       "      <td>0.278+/-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.825+/-0.01</td>\n",
       "      <td>0.556+/-0.03</td>\n",
       "      <td>0.289+/-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.825+/-0.01</td>\n",
       "      <td>0.6+/-0.02</td>\n",
       "      <td>0.289+/-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters          0.01               0.1               0.01\n",
       "Validation performance     0.837+/-0.01      0.573+/-0.03       0.278+/-0.01\n",
       "Test performance           0.825+/-0.01      0.556+/-0.03       0.289+/-0.01\n",
       "Optimal parameter(s)               0.01               1.0               0.01\n",
       "Optimal test performance   0.825+/-0.01        0.6+/-0.02       0.289+/-0.01"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_dt = get_summary_performances(performances_df_dt, parameter_column_name=\"Parameters summary\")\n",
    "summary_performances_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the optimal class weight for the majority class depends on the performance metric. For better visualization, let us plot the performances as a function of the class weight for the three performance metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_performances_plots(performances_df_dt, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Class weight for the majority class\",\n",
    "                       summary_performances=summary_performances_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are mitigated, showing conflicting trends between the class weight of the majority class and the performance gains. For AUC ROC and CP@100, a class weight close to the imbalance ratio (0.01) provides the highest performance for both the test set and validation set, but the lowest performance in terms of Average Precision. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression\n",
    "\n",
    "Let us follow the same methodology as above, using logistic regression as the classification algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define classifier\n",
    "classifier = sklearn.linear_model.LogisticRegression()\n",
    "\n",
    "# Set of parameters for which to assess model performances\n",
    "parameters = {'clf__C':[1], 'clf__random_state':[0],\n",
    "              'clf__class_weight':[{0: w} for w in [0.01, 0.05, 0.1, 0.5, 1]]}\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# Fit models and assess performances for all parameters\n",
    "performances_df = model_selection_wrapper(transactions_df, classifier, \n",
    "                                          input_features, output_feature,\n",
    "                                          parameters, scoring, \n",
    "                                          start_date_training_for_valid,\n",
    "                                          start_date_training_for_test,\n",
    "                                          n_folds=n_folds,\n",
    "                                          delta_train=delta_train, \n",
    "                                          delta_delay=delta_delay, \n",
    "                                          delta_assessment=delta_assessment,\n",
    "                                          performance_metrics_list_grid=performance_metrics_list_grid,\n",
    "                                          performance_metrics_list=performance_metrics_list,\n",
    "                                          n_jobs=1)\n",
    "\n",
    "execution_time_lr = time.time()-start_time\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select parameter of interest (class_weight)\n",
    "parameters_dict=dict(performances_df['Parameters'])\n",
    "performances_df['Parameters summary']=[parameters_dict[i]['clf__class_weight'][0] for i in range(len(parameters_dict))]\n",
    "\n",
    "# Rename to performances_df_dt for model performance comparison at the end of this notebook\n",
    "performances_df_lr=performances_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC Test</th>\n",
       "      <th>AUC ROC Test Std</th>\n",
       "      <th>Average precision Test</th>\n",
       "      <th>Average precision Test Std</th>\n",
       "      <th>Card Precision@100 Test</th>\n",
       "      <th>Card Precision@100 Test Std</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Execution time</th>\n",
       "      <th>AUC ROC Validation</th>\n",
       "      <th>AUC ROC Validation Std</th>\n",
       "      <th>Average precision Validation</th>\n",
       "      <th>Average precision Validation Std</th>\n",
       "      <th>Card Precision@100 Validation</th>\n",
       "      <th>Card Precision@100 Validation Std</th>\n",
       "      <th>Parameters summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.871396</td>\n",
       "      <td>0.017137</td>\n",
       "      <td>0.571129</td>\n",
       "      <td>0.028027</td>\n",
       "      <td>0.293929</td>\n",
       "      <td>0.010120</td>\n",
       "      <td>{'clf__C': 1, 'clf__class_weight': {0: 0.01}, ...</td>\n",
       "      <td>0.587376</td>\n",
       "      <td>0.871069</td>\n",
       "      <td>0.009955</td>\n",
       "      <td>0.497808</td>\n",
       "      <td>0.039734</td>\n",
       "      <td>0.276429</td>\n",
       "      <td>0.013190</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.870711</td>\n",
       "      <td>0.016332</td>\n",
       "      <td>0.604805</td>\n",
       "      <td>0.015834</td>\n",
       "      <td>0.296786</td>\n",
       "      <td>0.009813</td>\n",
       "      <td>{'clf__C': 1, 'clf__class_weight': {0: 0.05}, ...</td>\n",
       "      <td>0.609912</td>\n",
       "      <td>0.870584</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>0.550617</td>\n",
       "      <td>0.029466</td>\n",
       "      <td>0.278571</td>\n",
       "      <td>0.015085</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.870083</td>\n",
       "      <td>0.016028</td>\n",
       "      <td>0.613923</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>0.296429</td>\n",
       "      <td>0.008950</td>\n",
       "      <td>{'clf__C': 1, 'clf__class_weight': {0: 0.1}, '...</td>\n",
       "      <td>0.515544</td>\n",
       "      <td>0.869906</td>\n",
       "      <td>0.008720</td>\n",
       "      <td>0.579392</td>\n",
       "      <td>0.019886</td>\n",
       "      <td>0.278214</td>\n",
       "      <td>0.014156</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.868234</td>\n",
       "      <td>0.015572</td>\n",
       "      <td>0.621852</td>\n",
       "      <td>0.015687</td>\n",
       "      <td>0.297500</td>\n",
       "      <td>0.008770</td>\n",
       "      <td>{'clf__C': 1, 'clf__class_weight': {0: 0.5}, '...</td>\n",
       "      <td>0.712305</td>\n",
       "      <td>0.867853</td>\n",
       "      <td>0.008948</td>\n",
       "      <td>0.608950</td>\n",
       "      <td>0.023132</td>\n",
       "      <td>0.277143</td>\n",
       "      <td>0.015286</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.867643</td>\n",
       "      <td>0.015404</td>\n",
       "      <td>0.623081</td>\n",
       "      <td>0.016204</td>\n",
       "      <td>0.297143</td>\n",
       "      <td>0.008806</td>\n",
       "      <td>{'clf__C': 1, 'clf__class_weight': {0: 1}, 'cl...</td>\n",
       "      <td>0.576240</td>\n",
       "      <td>0.866861</td>\n",
       "      <td>0.008988</td>\n",
       "      <td>0.612264</td>\n",
       "      <td>0.023474</td>\n",
       "      <td>0.278214</td>\n",
       "      <td>0.016914</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AUC ROC Test  AUC ROC Test Std  Average precision Test  \\\n",
       "0      0.871396          0.017137                0.571129   \n",
       "1      0.870711          0.016332                0.604805   \n",
       "2      0.870083          0.016028                0.613923   \n",
       "3      0.868234          0.015572                0.621852   \n",
       "4      0.867643          0.015404                0.623081   \n",
       "\n",
       "   Average precision Test Std  Card Precision@100 Test  \\\n",
       "0                    0.028027                 0.293929   \n",
       "1                    0.015834                 0.296786   \n",
       "2                    0.014765                 0.296429   \n",
       "3                    0.015687                 0.297500   \n",
       "4                    0.016204                 0.297143   \n",
       "\n",
       "   Card Precision@100 Test Std  \\\n",
       "0                     0.010120   \n",
       "1                     0.009813   \n",
       "2                     0.008950   \n",
       "3                     0.008770   \n",
       "4                     0.008806   \n",
       "\n",
       "                                          Parameters  Execution time  \\\n",
       "0  {'clf__C': 1, 'clf__class_weight': {0: 0.01}, ...        0.587376   \n",
       "1  {'clf__C': 1, 'clf__class_weight': {0: 0.05}, ...        0.609912   \n",
       "2  {'clf__C': 1, 'clf__class_weight': {0: 0.1}, '...        0.515544   \n",
       "3  {'clf__C': 1, 'clf__class_weight': {0: 0.5}, '...        0.712305   \n",
       "4  {'clf__C': 1, 'clf__class_weight': {0: 1}, 'cl...        0.576240   \n",
       "\n",
       "   AUC ROC Validation  AUC ROC Validation Std  Average precision Validation  \\\n",
       "0            0.871069                0.009955                      0.497808   \n",
       "1            0.870584                0.008772                      0.550617   \n",
       "2            0.869906                0.008720                      0.579392   \n",
       "3            0.867853                0.008948                      0.608950   \n",
       "4            0.866861                0.008988                      0.612264   \n",
       "\n",
       "   Average precision Validation Std  Card Precision@100 Validation  \\\n",
       "0                          0.039734                       0.276429   \n",
       "1                          0.029466                       0.278571   \n",
       "2                          0.019886                       0.278214   \n",
       "3                          0.023132                       0.277143   \n",
       "4                          0.023474                       0.278214   \n",
       "\n",
       "   Card Precision@100 Validation Std  Parameters summary  \n",
       "0                           0.013190                0.01  \n",
       "1                           0.015085                0.05  \n",
       "2                           0.014156                0.10  \n",
       "3                           0.015286                0.50  \n",
       "4                           0.016914                1.00  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performances_df_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC ROC</th>\n",
       "      <th>Average precision</th>\n",
       "      <th>Card Precision@100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Best estimated parameters</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation performance</th>\n",
       "      <td>0.871+/-0.01</td>\n",
       "      <td>0.612+/-0.02</td>\n",
       "      <td>0.279+/-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test performance</th>\n",
       "      <td>0.871+/-0.02</td>\n",
       "      <td>0.623+/-0.02</td>\n",
       "      <td>0.297+/-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal parameter(s)</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optimal test performance</th>\n",
       "      <td>0.871+/-0.02</td>\n",
       "      <td>0.623+/-0.02</td>\n",
       "      <td>0.298+/-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                AUC ROC Average precision Card Precision@100\n",
       "Best estimated parameters          0.01               1.0               0.05\n",
       "Validation performance     0.871+/-0.01      0.612+/-0.02       0.279+/-0.02\n",
       "Test performance           0.871+/-0.02      0.623+/-0.02       0.297+/-0.01\n",
       "Optimal parameter(s)               0.01               1.0                0.5\n",
       "Optimal test performance   0.871+/-0.02      0.623+/-0.02       0.298+/-0.01"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_performances_lr = get_summary_performances(performances_df_lr, parameter_column_name=\"Parameters summary\")\n",
    "summary_performances_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79u0rXbem/aenp7Nt27aoi9FJkybxwgsvRO1n/fr1vPLKK5x77rncd999fPbZZ7Ro0YJ//OMfUeu1a9eOwsJCduzYYS/bvn07Xq9XxrIQopYMw2Do0KG89957FWo8V61axWeffUZqaipr1qzhl19+4d133+Xmm2/mggsu4Oeff672xtWZZ54Z1VXjxx9/pKioqE7jP9bOOykpKXZCnJCQEJX8zp49G03TOOOMMygoKGDGjBmMGzeuVt9Dly5dWL9+vf3voZRi/fr1djejLl26sG7dOnv9/fv38/PPP9vlQhwuSbSPc4sXLyYpKYnRo0eTkZFhP/r27Uu3bt34+OOPyczMpH379tx555385z//Ye/evXz99dfcd999DBs2jNatWwNwwgkn8Mc//pHJkycze/Zsdu/ezebNm7n77rv57rvvDnseyYceeoj8/HxefPFFIDTgxJQpU3j++ed54YUX2LFjBz/++CNvvfUWDz30EHfffbd98VrXsQgh6k9tzkOAXat9/vnn202xTz/9dHr37s3999/Pxo0b2bp1KxMnTiQrK6vCoDVhKSkprFixgr1797Ju3Tp7AB6/309CQgJXXXUVTz75JBs2bGDDhg088cQTQGgE2iM5XnFxMY888gg7duxg3rx5LFu2jLFjx1a6bk37Hz58OIWFhUybNo1du3axePFiFi9eTO/evaP2ExcXxyuvvMIHH3zATz/9xPLly9m/fz+dOnWKWq9du3ZccsklTJw4kU2bNrFp0yZ75OHwTVQhRM3uuOMOfD4fN9xwA6tWrWLPnj18/PHH3HXXXVx11VV0796dlJQUiouL+fvf/85PP/3Ehx9+yPvvv19tH9+xY8fy3nvv8dlnn/HDDz/w8MMPVzrQ69E41s47HTp04N///jcAgwcP5pVXXmH58uU899xzzJo1C6fTyT/+8Q/GjRvHRRddVCHOqlx66aUUFRXx+OOPs337dvtGw5AhQwAYPXo0ixcvZt68eWzbto2JEyfSp08fTj311FrtX4gKGnY2MdHQLr30UvXoo49WWrZw4UKVkZGhtm3bprKzs9WDDz6oLrroItWxY0d1ySWXqGeffVb5fL4K2y1atEiNHDlSZWZmqh49eqjx48erbdu2VRlDdfNoP/fcc6pDhw5R269du1bdfPPN6vzzz1eZmZnq2muvVcuXL690+8ONRQjR8Gp7Hvr5559V+/bt7TlNw7KystQ999yjMjMzVffu3dWECRPUwYMHlVJKrVq1SmVkZKhAIGCvv3btWnXZZZepzp07q/79+6vXX39d/fa3v1Uvv/yyUkopr9er7r33XtW1a1d10UUXqddee01lZGTYc7FWd7zy5s+fry666CL15z//WXXt2lUNHDhQLVmyxC6fOHFi1Lystdn/hg0b1MiRI1WnTp3UoEGD1KefflrpZ/3kk0/UpZdeqjp16qT69eun3nvvPaWUUnv37o2a+zUnJ0fdc889qlu3burcc89VEydOVLm5uVV+f5XFLIRQ6pdfflGTJ09Wffr0UZ07d1a/+c1v1KxZs5Tf77fXeemll1TPnj1Vt27d1KhRo9T8+fNVRkaG2rdvX6W/N6WUeuutt9SFF16ozj33XPXmm2+q7t27H/Y82pGO9fOO1+tV559/vtq4caPKzc1Vt9xyizrnnHPUXXfdpVatWqXOP/98deGFF6pXX31Vmaapdu7cqQoLC6M+b/l4wjZu3KiuuOIK1alTJzVixAi1efPmqPIFCxaoiy++WHXt2lXdfvvtKisrq9rvWojqaEpJRywhhBBNx5dffkmvXr3sUYE3bdrEmDFjWL9+/WF3OVmwYAHPP/98haaTQgghjty8efN45ZVXmDlzpj1HeXmmafLnP/+Z5cuX8/HHHxMfH9/AUQpRPRkMTQghRJPy8ssvs3z5cm699VYKCwt55pln6Nevn4zrIIQQMeK3v/0t+fn5jBw5kqFDh9KvXz9OPfVU4uPjyc7OZv369cybNw+n08lbb70lSbaISVKjLYQQoknZvn07jz/+OJs2bcLlctGvXz8efPDBGufIrYzUaAshRP3ZsWMHf/vb31i9ejX79u3D5/ORnJzM2WefzZAhQxg+fLjcJBUxSxJtIYQQQgghhBCiDsmo40IIIYQQQgghRB2SRFsIIYQQQgghhKhDkmgLIYQQQgghhBB1SBJtIYQQQgghhBCiDkmiLYQQQgghhBBC1CFJtIUQQgghhBBCiDokibYQQgghhBBCCFGHJNEWQgghhBBCCCHqkCTaQgghhBBCCCFEHZJEWwghhBBCCCGEqEOSaAshhBBCCCGEEHVIEm0hhBBCCCGEEKIOSaIthBBCCCGEEELUIUm0hRBCCCGEEEKIOiSJthBCCCGEEEIIUYccjR2AEEIIIYSILZZl8eijj7Jt2zZcLhdTp04lPT3dLv/888+ZOXMmmqYxatQoRo4cSSAQ4MEHH2Tfvn34/X5uu+02+vfv34ifQgghGo8k2kIIIYQQIsqXX36J3+9n7ty5bNiwgenTp/Pqq68CYJomzz77LPPnzyc+Pp4hQ4bQv39/VqxYQUpKCs888ww5OTlceeWVkmgLIZosSbSFEEIIIUSUdevW0bt3bwC6du3Kli1b7DLDMFi6dCkOh4OsrCwAEhISuPTSSxk8eHDUekII0VQd04m2aZqYpllpmWEYVZY1tliNLVbjgtiNLVbjgtrF5nK5GigacaTkPFe3YjUuiN3YjvW45Dx3ZLxeL4mJifZ7wzAIBoM4HKFLR4fDwRdffMGUKVPo27cvDocDt9ttb3vnnXdy11131epYcp6rWxLX4YvV2OQ8d2w75hPt8J3U8tLS0qosa2yxGlusxgWxG1usxgW1i61NmzYNFI04UnKeq1uxGhfEbmzHelxynjsyiYmJFBYW2u8ty7KT7LBBgwYxYMAAHnjgARYuXMiIESPYv38/f/jDHxgzZgzDhg076jg0TTvqfdSXWI1N4jp8sRpbrMYlaueYTrSFEEIIIUTdy8zMZMWKFQwZMoQNGzaQkZFhl3m9XsaPH8/s2bNxuVx4PB50XefQoUPceOONPPLII/Tq1avWx5IbinVL4jp8sRqb3FA8tkmiLYQQQgghogwcOJCVK1dyzTXXoJRi2rRpLFq0iKKiIkaNGsWwYcMYO3YsDoeD9u3bM3z4cJ588kny8/N55ZVXeOWVVwB44403iIuLa+RPI4QQDU8SbSGEEEIIEUXXdaZMmRK17PTTT7dfjxo1ilGjRkWVP/zwwzz88MMNEp8QQsQ6SbSFEEIIIYQQogkKBALs2bOHkpKSxg7lmBMXF0fbtm1xOp2VlkuiLYQQQgghhBBN0J49ezAMg1atWsnga4dBKUVhYSF79uyJau0TSW/gmIQQQgghhBBCxICSkhISEhIkyT5MmqaRkJBQbUsASbSFEEIIIYQQoomSJPvI1PS9SdNxIYQQQgghhBB14qWXXmLbtm1kZWXh8/k48cQTSUlJYerUqQ0ei8/n4/PPP2f48OENfuzjPtEuChShaRoeh6exQxFCCCGaHqXKHuXfV7Jcq2wdyyp7hN/7/aHXbndjfTIRI0qCJRQFikhyJ+HQj/tLWyFi3oQJEwBYsmQJe/bs4bbbbmu0WLKzs1m0aJEk2vWhKFhEdnE2JyScQJI7qbHDEUIIIRpedUluJWVa5DYuF1p+fnSyGy4vfa9FJsHh7cuvG9HETkWEpkFZmYooCW+jaahweel7APLy0BISovYlmp58Xz4Hiw+iazo5vhzSPGkkuZLQNekdKUQs8Xq93HDDDXzwwQcYhsErr7zCWWedxYIFC2jbti179uxBKcWUKVNIS0vj1VdfZePGjViWxTXXXEO/fv2i9jd16lT27duH3+9n9OjRDBgwgPXr1zNz5kx0Xeekk07i/vvvZ86cOezevZvZs2dz4403NuhnPu4TbQCn4eRg8UF8po80T5qcfIUQQjSO6hJdnw9KSqKT1chtytfqlluulVuuRW5TLtGFiGS3tEwrn+yGtykpQc/Li052w8+RiW/Ee1W+rLqv5Ei+RwBd/pY3ZaZlcqjkEAX+AuId8eiajqUssoqzyCvJo2V8SzwOj/Q9FSJGJCYmcs4557B69Wp69OjBqlWruOWWW1iwYAGdO3fm/vvvZ8GCBbzzzjv07NmT/fv389prr+Hz+fj973/PeeedR7NmzQAoLCzkP//5D7NmzULTNNasWYNSiqeeeopXX32V1NRUZs6cydKlS7nuuuvYsWNHgyfZ0EQSbQ2NRGci+f58/Jaf1vGtpWmREEKIIxcMohUVlTVfjqi9jazd1cqVVaBUWQJbUICRl2cvr5AcR9bslr6vkOyGl+v6YSW7AFZpgq1KU19V+t6M9xDwFUeEXH49K+o9qmwde5ty+7TKbWOVfj8WVrXrKaVCDxQlDhfK2QY38bX6fOL44TN9HCg6QNAKkuhMtJfrmk6CM4GgFeTnwp+JM+Jo4WlBnCOuEaM9NlT8Xauy33Q5GhXPKZXd0Ci/Xvnzgmh6hg8fzkcffYRSinPPPdeef7p79+4AdOrUiW+++YZWrVqxbds27rjjDgCCwSC//PKLnWgnJCRwzz338PTTT1NYWMjgwYPJzc3l0KFDTJ48GQj1zT7vvPMa4VOWaVLZZoIzgeJgMT8V/MQJCSfIiVcIcUQsy+LRRx9l27ZtuFwupk6dSnp6ul2+adMmpk+fjlKKli1b8swzz+B2u7niiivsPxInn3wyTz75ZGN9BHEkLAutpAQtNxetqAgFWIYeuhTVtNBFaURiqwB0DWXogG5fstoXsuUuYl1ujUJX5MWoVnNCatUucQ2XRV5Ml09+q5Kv5ZPvzUcphVb6OTXKnkORRixXikquw6PYF+BaFcsjXpd/Dj+VBItxqipuYIjjVoGvgIPFB3HqTuIdld9kcegOEvVE/KafvQV7SXGnkBKXglN3NnC0IVG/u/IJbfnfZSW/0bInFXUTKvz7tizLXl8pRdAdJKswq/Ly8HEtVVaurKjfc42f5zDWjZStZZObm1tpWWWtTfVKJkfS9OjjVpr0l/4Xta9KWsBE7t/v9pNTmFNpHFXdRDjcY9jLDuMYcf44LGUdN61xu3TpwgsvvMDixYu55ZZb7OXbtm2jVatWbN68mdNOO422bduSmZnJxIkTsSyLt99+mxNPPNFe/9ChQ2zbto0nn3wSn8/HVVddxaBBg2jVqhXTp08nMTGRb775hvj4+NDfrUa6ydOkEm0Aj8NDwAywz7uP1vGtSXQl1ryREEJE+PLLL/H7/cydO5cNGzYwffp0Xn31VSB0QTV58mRefPFF0tPT+fDDD9m3bx8nnXQSAO+++25jhi5qQSmFqUxMZWJZJqqkGCs/Hysvm6AZIGBoBJylf7gPI88LX5SVT0jD74sdxRQUFVS6TVUJaZXJaMQ24WU6OmhlF3Rl+67+gjnBlUDAEaz9B20gmvI3dgiiAdnNwn15eBweDN2ocRuX4cJluPAGvOT782ke19yuZIlqgRFx0yl80yoySY1saRFZ7nP6OFRwqKwcVXbzCysqMQ6LTFLDN68iRf6Gq7tpZf/mteibU5qm4Q648Zm+CucPDc1O/DRDq3Qf9SnRlUjAGaiwvLIkqLLa9FqvF3mTopRlVjxZR65TEizBZ/qO6riVOZptAbyGlyQr6biqHBw0aBDLly+nXbt29rKlS5fywQcfEBcXxyOPPEJSUhLr16/ntttuo7i4mD59+pCQkGCvn5aWRlZWFtdffz0ej4fRo0fjdDr54x//yH333YdlWSQkJDB58mTi4+MJBAK88sor3H777Q36WZtcog2hPtu6prO/aD/NzeYku5OlKbkQotbWrVtH7969AejatStbtmyxy3bt2kVKSgpz5szhhx9+oG/fvrRr146NGzdSXFzMjTfeSDAY5J577qFr166N9RGaJEtZBK0gPtMXqg0qfR+wAgSsAEErSNAKYikLzR9AKyrElV8EZgDNcKLi3OjO0N8Pt6aj1XENQ6IzkaDDrNN9CnG8OFB4gKJg0RFVkHgcHixlkV2SDZS1wKhKOGGNfA/RN6k0NPyWHwsr6maWZmiVbtOQ3A43LsPV4Mc9UrVpdl66sN64jdj8zgzDOKwburFm6PdNLIMAACAASURBVNChFZaZpllhBPDx48dHtQwEuPPOO6vcr6Zp3H///RWW9+jRgx49elRYPmfOnNqGXKeabHZp6AaJWiK5vlxyfDnEO+JJdicTZ8TV6i6pEKLp8nq9JCaWXewZhkEwGMThcJCTk8P69euZPHky6enpjB8/nk6dOtG8eXNuuukmRo4cye7du7nllltYtmwZDkf1p2HDMEhLS6u0zOFwVFnW2BoytnDSbFpm6FmZBMwAQRUkEAzYibSFRUF+AUGjtHZWA01pOHUnLs2FYSocJX6M3Hw0vwIjAXVCczAa5m+C4TBISU5ukGMdjliNy1+QTWpqKgmpsfkbEHXLb/qPaqrWcP/tuuQyXI3WHF2IY9HUqVPJy8tj2rRpjR1Kg2iyiTaE7oaET7oBM8D+wv1ohJYluULNNI6XPhFCiLqTmJhIYWGh/d6yLDthTklJIT09nTPOOAOA3r17s2XLFq677jrS09PRNI3TTjuNlJQUfv31V9q0aVPtsUzTJCsrq9KycNOpWFQXsdkJdGkzbqWUXfMcWQNtKrPSvsK6pmNoBpqm2c+JqYnk5OdEHMRCL/GhF3gxCgvxaRqWywXhGyCBis0c60tKcjK54cHQYkisxuVWJjk5OZRY1d8Iqek3JoQQomE8/PDDFZa9/PLLjRBJw2jSiXYkp+HEaThRSuEzfez37gcNkl3JJLgScBtuSbqFEABkZmayYsUKhgwZwoYNG8jIyLDLTjnlFAoLC/nxxx9JT09n7dq1XH311Xz00Uf88MMPPProoxw4cACv10vLli0b8VM0nnDyHH62E2gzSECFap/DtdM2BWihWimNssQ5zog7/KaZSqH5/ejeQoz8gtDI3w4nZnx8rUfoFkIIIYSojiTa5Wiahttw4zbcKKXwBrzk+fNQKAzNwKE7cOgOnJoTh+HAqTvt2pJw7Ykk5EIc3wYOHMjKlSu55pprUEoxbdo0Fi1aRFFREaNGjeKJJ57g3nvvRSlFt27duPjii/H7/UyaNInRo0ejaRrTpk2rsdn4sSYygS4KFOH1e0PNt8PNuCMS6PKDAEUm0IZm4DScdd+3MRBAy83DtXcfBIMow8CKi5P5mIUQQghR546vq7w6pmla1Ch/4ZFog2YQn/KhAqWjT5bWtIQvHHVNx9ANnLoTh+bAaYSedV1HRydghi42pS+4EMcmXdeZMmVK1LLTTz/dft2rVy8++uijqHKXy8Wzzz7bIPHVNbv5tmViYWFZVoUEOmgG7RF2NU3D6/CSW5SLrumhB3r9JdDVMU304hKM/PzQc2oqluEAt7vhYhBCCCEOh2WBaaKVWKBXPo2diH31kmjXNMfswoULmTVrFs2aNePKK69k5MiRNW4TCzRNw6GFvjInVQ9+EZ7+IWgG8ePHCkRP8VDoKCQ3PzSPoEN34NAcuAwXhmbgMlxlF6YRNeSNMWqlEKLp8Jt+/KbfTqD9ph8T006gy6uQQDuiE+hEZ+XTuDQIpdBKSkJNw73e0PQ9LhdmYiIqPr5B+10LIYQQUZQCy0IzLbBMtKCJFgyGZrsI+NECQVAWoOGI09FOSADnkQ8EKBpPvSTa1c0xm52dzQsvvMDHH39MUlIS119/Pb169eL777+vcptjTbgpuUHlNdaR8wiGa4iKgkX2wD8QGtAnPGVEuDml3Wxdd9qv7WNJQi6EOAKmZdqzLwD2uaSqBDqWaT4/emEhRoEXzTSxHA4sj/S7FkII0YDCtdHhZNoMJ9FBtGDpM0TcwlYozQBdRxkGyu2O6NLkb5SP0NBeeukltm3bRlZWFj6fjxNPPJGUlBSmTp1a47Y7duygoKAgJqdMrZdEu7o5Zn/66SfOOussUlJSAOjcuTMbN25k06ZNVW5zPDP0qhPySOGmmz7TZyfllQk3UXfpLgzdsJ8ja8jDgwgJIZoupRQF/gIOFR8CDRIcCcfmeSEYRC8uxsjLR/MHULqB5XZJv2shjlJNLQ0///xzZs6ciaZpjBo1ipEjR9plGzdu5M9//jPvvvtuY4QuRP05jNpoTYHSADSUoYcSad1AxcfefN2NbcKECQAsWbKEPXv2cNttt9V626+++ormzZs3nUS7ujlm09PT2b59O4cOHSIhIYF//etfnHrqqdVuI8qaadYkXCvuM31YwdBrCwsdPar5p67pFWrI3X43RcGiqBpyGdhNiONPUbCIX4t+JWAF8BieY2+8iPCUXHn5GMXFKA0sVxwqQS5ehKgr1bVONE2TZ599lvnz5xMfH8+QIUPo378/zZs354033uDTTz/F45GmruIYdFS10Xq52mhxpILBIM888wx79+5FKcUtt9xCZmYmr7/+OuvWrUMpxYABA+jXrx9Lly7F6XTSvn17OnTo0NihR6mXLLa6OWaTk5OZNGkSEyZM4IQTTqBjx46kpqZWu01VDMMgLS2t0jKHw0FaWhpWkYXT54wa1KyxORwOUlNTGzWG8MBu9jy1lsnBooMEHAF7PlooS8jtGnLDhUN3hGriwyOt6/U/0nr43zPWxGpcENuxicbhM31kFWdRFCjC7XCT6EyseaNYoRSazxfqd13gDU3J5XRiJiQ0dmRCHJeqa51oGAZLly7F4XCQlZUFQELpb7Ft27a89NJL3H///Q0ftBDVKV8bbVqhVlE+P4a3CNehQ6FEm1AXzqZYGz1361z++t+/1uk+x5w9hlFnjTqsbRYtWmTnjHl5edx+++28//77LFu2jBkzZtCiRQuWLl1Ky5YtGTJkCM2bN4+5JBvqKdGubo7ZYDDIxo0bef/99wkGg9xwww3cfffdmKZZ5TZVMU3TPsGXl5aWRlZWFtnF2Xj93phKtFNTU8nJyWnsMCpITU2lOL84all4YDd7vltUpc3WNTQchgODiGQ8YqozTdPKEngNu+95+bKo12h2U9YWaS3Izs6ul899NML/n8Wi2sTWpk2bBopGNKagFSS3JNQP26k7SXQdOwm25g+gFxVh5BegBYOl/a490u9aiHpWU0tDh8PBF198wZQpU+jbt6+9fPDgwfz000+HdazaVJzk6Xn2gLGxIhYqTirTZOMK10aboedwbTSBQKhWOhgMradUeMIg0EK10IZhkNSqVczVRvvzc2ie1py4xJTGDqVB7dixg40bN/L9998DoZwvLy+Pxx57jNdee43s7Gx69uzZyFHWrF4S7ZrmmHU6nVx11VW43W5uuOEGmjdvXuk2ovFFDuxWm5HWLVU2sJtSivB/4eQ5/FpDs19XNqJx+W1ytBxyc0MjtYcHagLQdI3I/8JTqIXXCyfqOjp66clTR6+zGwDhkeSFiFXFwWJ+8f4CWmgk8GOiH3YwGJqKKy8fze8HTQ/1u5YpuYRoMLVpaTho0CAGDBjAAw88wMKFCxkxYsQRHas2FSe5ebk4DWdMJdqxXHFy3MVVRW205vejlybS1ddG62BU0U0qACluN7n5+UcWWz1yESQ7Kxu3z6x2vbqqOBl11qjDrn2uD+np6bRs2ZLrrrsOn8/HnDlz8Hg8LF++nMceewylFNdeey0DBgxA07SYvR6vl0S7pjlm77jjDu64444atxHHjppGWj9a4ZHawz+kyOnSVMR/lmnZiXv5dYAK21R2AyDsiG8AaGWJeWPdAIj8HKLpKgoWsd+7H5fhwqlXfaMsJoT7XRd4MQoLUZqG5XKhpGm4EI2iutaJXq+X8ePHM3v2bFwuFx6Px/5bJsQRkb7RIsLll1/OU089xR/+8AcKCwu56qqrcLlc9oxVzZo14/zzz6d169acddZZzJgxg/T0dLp3797YoUeRkcbEMSUqkYyBXLK+bwCEHe4NAABX4vHdj0hUr9BfyM+FP+NxeHDoMXqqVypUG1HgLet37XBixsuUXEI0tppaJw4bNoyxY8ficDho3749w4cPb7xgg0H0oiKshISqay1F4ymtjcbvRyspOYLa6OO/b7QIGTp0qP168uTJFcpvvPFGbrzxxqhlF1xwARdccEG9x3YkYvTqS4hjS6zdACgOFmOq6psZieNXga+AX4p+Id4RH5sjigcC6EXFOPLyIRhEGQZWXJzURggRQ2pqnThq1ChGjaq8ienJJ5/MvHnz6jW+SEZ+AY6sbJQrj2Bac6wEuVnXoGpZG+1ISsKZn4/URoumQhJtIYQ4juT58jhYfJAEZ0JM9WXENEP9rvPz0YtLULqO5XJLv2shxNEJBjHy8jATE8FSOA8exIrzEGyRhnLFeJeZY0FNfaMDwdDAY1RTG+1xgqahEhKwwgOSCdEESKIthBDHiZySHLKKs2InyVYKraQkNCWX1wsKlMsVuiAWQog6YBR4AS1UI6qD6UhE8/lx/bSPYEoyZnLS8d2cXKmoh2Yp8PnQfL6yZSpyPQsshWZZoZpoywqVWxYoK7Q8vB+rdF2tmr7RLpfURgtRBUm0hRDiGKeUIrskmxxfDgnOhEYfWVzz+dGzsnHt/QnNNEun5JKmnEKIOhYMYuTmYsV5ohYrtwvT5cTIy8Mo8BJs2QIr3lPFTupRuSQ4KuklIjGOXMeyDisJBmUPmhpOhh0FXpz5eWgKlH3eDU9opYWWaZSek8teK01DEbphoQwtVC7nbSGOmCTaQghxjMspySGnpJGT7GAQvbi4dEquAHpKKpbUdAgh6pHhLcSuzS5P07DiEyBo4vzlF8z4BMzmqWXNyWuRBFdZG2yaoZkSfH4cObmHlQSXHhxNhZtZly2zE2FdO6okWJpoCxEbJNEWQohjXHGwGLfhbvgkOzwlV14+RnExSgPLFYdKcKHiPRDwN2w8QoimIxjEyMkNDaRYHYdR2pzch+unfaH5lI8mCY6oDdacLrRAoDQRlppgIUQ0SbSFEELUnlJoPl+o33WBN7TI4cCU+a4FoW4MFhaWUljKKn1tYaGwlImpLJRSmOHlEWWWvW3oYarQtIimMkvXid6mlcNDpxbpjf2RRSMJ1WZT61Yzyu3GdLlCNdN1lQi73aE+ykKII3b77bdz0003Rc2B/fzzz9OuXbsK0waOGDGCv/71r8ybN4/u3bvToUMHu8zn8zFmzBjmz59f5bE++eQThg4dys6dO/n2228rTBVW1yTRFkIIUSPNH0AvKgo1Dbf7XXuaVK2NqUzyAgXk+PPICeRREPASVGZpMmhFJYmWUpiYoaRSWSisiCTTLEtES7czlYXb7aKwpMhORFVUsmmFktGIhDOyLLz/yH1GJruRxw7HZNWQ7Ib3hw5B0yz3+ayyWIiOp6FoaPz7xK/x0KLBjilihGnWrja7PKlpFiLmXH755Xz22Wd2oh0IBFi5ciW33nprlduMGzfuiI71zjvvcOmll5KRkUFGRsYR7eNwSKIthBCicsFgaEquvHw0vx80HcvtgsO9uI1RJaaPnECenThHPucG8snx55EdyCXXn09OII+8QEG9JpI6OrqmoWt66WsdndL34UfpckPT0dAwopZr6JphbxNap/RZ0zAwcGgODD1i/5qGjo6hGaF1Ircp3X+cO45gIFjpsTVNx6B0/5pRSUyhYxhU3H/5dcLvy/ZvRMVY9h2EPmcbZwKp7pR6+/cQsSvcmkbGgBDi2HfxxRfz+uuvU1JSQlxcHN988w2ZmZn86U9/wufzkZ+fzw033ECfPn3sbaZOncqAAQM455xzeOyxxygoKODkk0+2y9evX8/s2bMBKCkpYfLkyWzcuJHs7Gz+9Kc/8dvf/paFCxcyZcoUPv/8c+bNm4fL5eLkk09m4sSJfP7556xatYqSkhL27dvH2LFjGTp06GF/Nkm0hRBClAn3uy7wYhQWojQNy+VCxXjTcKUUBUEv2f48ckuT5exAOGHOLV2eT77l5VBxNjmBPIrNkkr3ZWg6yc4kUp3JNHclc0biqaS6kkl1lj5coUeSIxGHnQxWnuzqpYloWTIZUVYu2U1NSSE3L6+Bv7mapSQnx2RcbiVjADRJR1qbLYSokXvuXOL++tc63WfJmDH4Ro2q+phuN7179+brr79m8ODBLFmyhMzMTAYPHkxmZiabN2/mzTffjEq0w5YuXUq7du249dZb+e6771i3bh0Au3bt4pFHHqFly5bMmTOHFStWcN111/H222/z2GOP8d133wGQl5fHrFmzeOutt0hISOCFF15g4cKFeDwevF4vzz33HHv37uX++++XRFsIIcSR0fwB9Pz8UE2RUiiHEzO+8abkCliBiFrlPHLt2ub80udcckrLQ7XN+QSVWem+4nQ3zV0ppDiTaBWfRlv3iWWJc4XnJJo5EmNjHnIhRAWh2mwltdlCHEeGDx/OjBkzyMzMpKCggF69ejFnzhwWL16MpmmYZuV/33ft2kXPnj0B6NixIw5HKLVt2bIlzz//PB6Ph19//ZVzzjmn0u1//vlnTjvtNBJKKxO6du3KmjVr6NChA2eeeSYArVq1wu8/shu7kmgLIUQTpxcW4Th4EKUZoVqiOr6AVUpRZBaXJsa5EQlzrt1MO7s0YQ4n1AXBwir3l+xsZifHbeNPpIvr7Kja5hRnEs1dKfZrj1FW8xWrtbNCiFqwa7MbYU5sIZoA36hR1dY+15fTTz+doqIiPvzwQy677DLeeOMNhg8fTq9evViyZAlLly6tdLv09HS2bNlC7969+eGHHwiWTms3ffp05s2bR0JCAo8//jhKhbp96bpuvwZo06YNu3fvpri4GI/Hw/r16znllFMA6mQmF0m0hRCiqVIKIye37MLVYdRqs/CgYHaC7M+Nrn0O5FFgFfJrUVYoqQ7k4bcCle7LqTlLa5OTSHUlc2JSa5qX1i6nuJJIdabQvDRhTnWlkOxohkOvXZxCiOOL7i1EarOFOD4NHTqUGTNmsGDBAjweD88//zzvvPMOrVu3Jjc3t9JtrrrqKqZNm8Ztt91G27ZtcTqdAAwePJjf//73NGvWjNTUVA4dOgTAOeecw//7f/+PG264AYCUlBRuuukmJkyYgKZpnHzyydx22218+eWXdfKZNBWZ1h9j/H4/WVlZlZalpaWRlZXFoeJDeP1e4hyx05cnNTWVnJycxg6jgliNC2I3tliNqzhYzOltTscqsqpdr02bNg0UkThStTnP7SvYh6UsnIaz9jsOBnEcykYvLMRKSCDLn8sB36FKBwErP1hYdYOCJRrxpLqSaeFpTjM9kebOZFJKE+fIhLl5aSKdYMQ36PzfsVyjHauxxWpcbuXH0SIdT3L1o47LeS721eY892P2ThJ+PgBx8TGTaMfqb0PiOnyxGpsLP+5WZ+BOTK52vaM5z3333Xe0bt36iLdv6g4cOEDHjh0rLZMabSGEaGI0nw/ngV9RlsIX7+aF/83knT0LKqxX20HBUkuT5xRnM1x6aE7ZWL1oEUIcmwxvIShiJskWQoiaSKIthBBNiF7gxfnrISyXi73mIe5fO40t+T8w8qQhXJh2XkQf52QZFEzUjmmiBYOhAfUCATR/AC1Q+vAH0AN++3XUehHr6vY2fns9vcJ+ordRzRI4+NqrUH1FjzhOOPK9KLcbmQVbCHGskERbCCGaAsvCyMrGkV+AGR/PF7+u5NHv/4Km6fzlnMkMaHVRY0coasM0q0w8tUAwOqmNSmAjtwlGJbUV91O6XWlS7LQsWpSUlNuXv2w7s/ouKodDGTrK6cRyOlEuF8rpDL13Oe3XyuVk4+49HHQ56GJIf/0mQ6lGmwVBCCGOhCTaQghxvAsEcB48hOb3UeRx8cwPM5j302I6J53F050ncZLnhMaO8JijlZQQt30Xus8Xkaz6q6+1LV/bG4hOhp2WRVpxSaXr1U9Sa1RIYK2I18oRejY9HvyaFlpWWeJbflnpsxXxOvRwYDld0ctcEes5HFDLxPmPd9yHjsXf3O46+z6EEKKpUko16Hgpx4uahjqTRFsIIY5npolr/wEUsENlc/+/p7HNu5Pr00cy4fTrceryZ6C2HIeyaLZyNUkrV5G4dj36YcyraTkcUUmonZg6HGXLPB6CHk+VSWiFZU4nqjRxtSKS2UqTZqcTy64hDq1T276usdjffvbLz+BWRzavqRBCiDJxcXEUFhaSkJAgyfZhUEpRWFhIXFzVA27XyxWWZVk8+uijbNu2DZfLxdSpU0lPT7fLP/30U9566y10XWfEiBGMGTMGv9/PpEmT2Lt3L4mJiTzyyCOceuqpRx2L5vOjBYNyS0EI0STpPj+YJp/m/ZOpW1/CrbuY0fVxerc4v7FDi31KEbdtO0n/XE2zlauI3/Y/APxtWpM9/Dd4z+2GlZAQnfhGJb1lyW9tktpYTGhF01XTtdznn3/OzJkz0TSNUaNGMXLkyBq3EULEnrZt27Jnzx4OHjzY2KEcc+Li4mjbtm2V5fWSfn755Zf4/X7mzp3Lhg0bmD59Oq+++qpd/vTTT7N48WLi4+MZOnQoQ4cOZdGiRcTHxzNv3jx27tzJ448/zqxZs446Fs3rxZXzK5ziBudhTH0jhBDHgeK8Q0zf8TKfHPg/MlM6Mb3TA5wQ17Kxw4pZms9H4tr1oZrrf67GeSgLpWkUdTqbX269kfwLe+A7LV36ijayt//6IQ5Mbp1wd2OHctyq7lrONE2effZZ5s+fT3x8PEOGDKF///6sXbu22us/IUTscTqdnH766Y0dxnGpXhLtdevW0bt3bwC6du3Kli1bosrbt29PQUEBDofD7hOwfft2+vTpA0C7du3YsWNHncWjBfw4fzlAoM0J4JCqbSFEE6EUd617mDV5W7j1tDHcetq1OHQZPKo8x6Esmv1zNUkrV4eahPt8mB4P3h7dyb+wJwU9z8NMTWnsMEWEf/xzDToWt05o7EiOX9VdyxmGwdKlS3E4HPb81wkJCTVe/wkhRFNSL1mn1+slMTHRfm8YBsFgEEdpknvmmWcyYsQIPB4PAwcOJCkpibPPPpsVK1YwYMAANm7cyIEDBzBNE6OagVEMwyAtLa3SMofDQVpaGqowlWLLj9twoYpKME9q0+jJtsPhIDU1tVFjqEysxgWxG1usxuUOuHE4HDRLa9bYoRyXamoeuWnTJqZPn45SipYtW/LMM8/gdDobvEnlwZy9rM7bzO3txjG+3bX1eqxjilLE/W8HSStX0WzlauK3/gCA/4TWZF82mIILe1LYtTPK5WrkQIVoPDVdyzkcDr744gumTJlC3759cTgcNW5TldpczxUmN8PpSUCPoXElDIdBSnLszS8ncR2+WI3Nn59D87TmxCXKzd5jUb2crRITEyksLLTfW5Zln2S3bt3KV199xf/93/8RHx/Pfffdx2effcaIESPYsWMHv/vd78jMzKRjx47VJtkQaroUvpNaXlpaGllZWeTk5FCSn4srPhnN54P8PAIntG7UZDs1NZWcnJxGO35VYjUuiN3YYjWu4mAxaZ40svIr/32EtWnTpoEiOr5U16RSKcXkyZN58cUXSU9P58MPP2Tfvn1s3769wZtU/nPvPwDoL1N3ofn8JP5nA6n//g+tv/oG18FDKE2juMNZ/PL768m/sCe+dqdKk3AhSlV3LRc2aNAgBgwYwAMPPMDChQtrtU1lanM9V5BXgOELohmxk2jH6rgKEtfhi9XYXATJzsrG7TOrXU+u52JTvZytMjMzWbFiBUOGDGHDhg1kZGTYZc2aNSMuLg63241hGDRv3pz8/Hw2b95M9+7defDBB9m8eTN79uyp87iU241WUoLzl4METmjV6DXbQohjU3XNI3ft2kVKSgpz5szhhx9+oG/fvrRr1465c+c2eJPKb/evpI27JWckNM3BiBxZ2TT75+rQ49//QS/xYXk8FJyXycGbfkf+BT2kSbgQVajuWs7r9TJ+/Hhmz56Ny+XC4/Gg63q12wghRFNTL5nmwIEDWblyJddccw1KKaZNm8aiRYsoKipi1KhRjBo1ijFjxuB0Omnbti1XXnklXq+XF154gdmzZ9OsWTOeeOKJ+ggNFRcnybYQ4qhU1zwyJyeH9evXM3nyZNLT0xk/fjydOnU64iaVRypQXMi/cjZyWZv+TWe6DqWI276ztEn4KuL/W9okvHUrcoYMIv/Cnjj6XkRuSUkjByqOltvtQqfu5hQXFdV0LTds2DDGjh2Lw+Ggffv2DB8+HE3TKmwjhBBNVb1c4em6zpQpU6KWRY5mN3r0aEaPHh1V3rx5c95+++36CKeCqGS7TWuooYm6EEJEqq55ZEpKCunp6ZxxxhkA9O7dmy1bthxxk8ra9F0sdhYTtIK4jLI+xd8eWEOxVcJv0i9plH5nDdbfzecjbs06PF9/S9zXK3H8cgClafg7dyB3wq2U9L2IQMYZoGk4wnG53fUf1xGI1T6CsRjX3958GX9BNqmpqSSkVv77EEenpmu5cMVJeeW3EUKIpqrJVuequDi04hKc+3/BbNYM5XahnE5JuoUQNaqueeQpp5xCYWEhP/74I+np6axdu5arr76atm3bHlGTytr0XcwuyMZSFk6jbArDz/+7FLfuoqP7zEbpd1af/d0c2Tk0++camq1cReLa/2AUl2DFuSk4rzsF14+l4ILzCTaPGKQwP79B4jpasRpbrMblVmZoHBar+r/b0ndRiMMXtEx8lo+AFUTXNHRNR0cPPWsaOoa9XAhRuSabaAMoTxwEgziyc9GUBZqG5TCwPB6Ux4NyOlFOhwyOI4SIUlOTyieeeIJ7770XpRTdunXj4osvxrKshmtSGQzyj6x/c15qFzxGXP0dp6EoRdyOXTRbuYqklavx/HcbmlL4W7Ugd/AA8i/sSWFmF5RbRglvKl5/+30MZfLH/zexsUMRokEopfBbAXyWjxLLT4lZQonlx2f6KDFDy3xW6LWerZPjzaOk9L2vdP3Qs48Sy4fP9EeUh/fpC+3P8hNUwVrHFp2Ah14bmo6GhqEZaJqGgY5hGGhKK12uRyTvGrpmoFOa0Ectr+x1+aS/6mNqUcurPmZ8nAe/L1DpTQWD0v1pOhql+ytdrmta9DEj1on6PPZ7o9LlhmbYMUbG3MaVQMt6/P9KtqL74wAAIABJREFU1K8mnWgD4HBgRTbfNE30omK0/AI7wbbi4lBxbpSuh2q8NQ2la6FyTY9+r+uSmAtxnKupSWWvXr346KOPatymvuzN2s6ekv1cm35VgxyvPmh+PwnrN9lTcLkOHASg6OwMDt40jvwLe1JyRjs53zZRq9duQMfij40diIgJh3zZFJrFtHQ1J97habDjmsqkKFgcSlzDya9ZlvBGJr92cht+XT5ZtnwVt7WXh9ZXqCOKM0534zZcuHU3HsONWw+9jjPcpLlScRsu4krfu3UXcYbb3sapObGwUEphKguFFXpWChMLS5U+wq9RZcvs9yYWCofTQYmvpHQ/leyvdL2o/SllrxO0zHLHLN23UhHrl4uB0D5MZZYdU1mYlX2mI/x+61NzZzLrRq5q7DDEEZJEuzzDQBkGKtyHTym0YDCUeCtCNd8oIHxxV/ZaI3THETQwdJShg66jdCP0XPpeMwx0b2FomUYoOaeSZF0uIIUQR+DbPV8DcFGL8xs5ksNj5OSSVDpKeOKa/2AUF2O53XjP68bB68dQ0Ot8gi2kP64QoszXv67i3s1T8VsBAOINDy3dzWnham4/t3CHXic7mxGwgmXJa7laXZ/lpzj82vRTbJXgK01+i0uT3cikOKACRxSzoenE6XEVE1zdTbzhIdWZHJHshspCCXIo+fWUbhuVOBtuPKXrt0pugb/IF1pfd8XMgJix2g0FQrHl5ObaiXnFmwdm1PKomwXl1gkl8RXXMe19m5XeDIi+wRBaPz1O/uYdyyTRrommhfpuO0N9H2t9r8uyQCk7UcdSaCiwFIYCR25uKJFWCg0NpZVL3hWhhLs0QQ/VpjtCiXnpzYBwefkadjt5DyftQoimw7L45sAq2sW35WTPCY0dTfWUwr1zN0nhJuHfb0VTikDLFuQO7kfBhT3xZnYpu/EphBAR/u/gSu7bPI2MxNMYfcrlZPmz+dWXzaHS5/8WbOdXXzZFZnGN+3LpznK1unF28pviTKa1u7SmtzThjdNdpCQko/yKOMNVeeIckSzHlb53626cev1efqfEJ5MbiM2ENpaFm4MbsXFfAgAX/sYOQRwFSbTrS0SCWz45V/HxWIFa3AUtTdSxQg8t6ENTgLKia9cVoGloSqHCybumldWu6xrKUdrk3XBE1a6HlpfWtLvj0Hy+0DbSHF6IY1KRN5u1+d8xtu0VjR1KpTS/n4QNm0P9rf+5Gtf+AwAUnZXBwRuvDTUJP/P0Y+Ocow6/mWF1TROt0lqQo6GOIKaamMrEssyyY9Rz88ra7F9hxWAjT9GQlv3yFZO+e4qOSe15tdsTNHMkVLluUbCYX/3Z5P1/9u48Por6fvz4a449s5sTORTkUvBApYBXEbAKthX9FkUNUAEP1J/CwxYUFVTkBkWUQlXUr6LiAZbWFrBqq8VSYr8WrajYQuTw4JIj5ya72d2Z+f2xRxKSbBJIspvwfmoeOzvzmZ33hmSy788ZKq2SUFd2n3ao9mOa1CuVW2iFEMkniXYqi3Ufj977ayTsDX0dy4q2sBNpXbcslFhru2nFX0n1B7AXl9Teuo4CuoalKpHWdKVqsq6DqoB61Pj1WMIee6mq3eGP3hZCNIl/fZ9H2AozOCd1uo1rhUV4/28z6Xn/h+fjTyu7hA/4EQfHjqL0xxcmp0u4ZWFFu+9ZsW2i4/SsyoRPid4PLSysaEVmzLF2yVSi/1UVMkKEzGPrilrndZrg/mpYBgaVifbRcTeHqteo7XqZ6enYVBVNlZVCGiIUCrF9+3ZKS0tJT0/n9NNPx25vvZMHrt33V2b85wn6Zp7FU33nkKa7E5Z36y666qe0UHRCCBEhifaJQFHiy5ZZVP9QUi1ZT0vDCNcxy2SsdT3awq6YIZRg9BXMaOKOSXy8usVRCXs0lPh1rSoXj7S6A9Ex7ZUJuBXtGaAGKtBLSiPnVu0aHyujUDk5Xew9Rz9gWkr15/EoYpUAdSX/UgEgWhvLIm//R3g0N30zz05qHPqOXZz07vt4P/o/3Fv/G+kS3i6HomE/oXTghfj698VyHueM6NExbbEEOFGiHNu2hXTKw2WRNLfKbLWqEknaNFVDJbId2x9LitXoTLNVt5tSVkYWhWZhk75mU8hKz8JjeJIdRjWvLfktWTYbBZpDWrbr8eGHH7J48WK6deuG2+2mrKyMXbt2MWXKFIYOHZrs8Brt93vfYfZ/f8P5WeextO8s3G1hZQUhRJskibZomKMSz2NuXa9LrLtjLJmPvWg0iVdC4Ui3dot421LVckq8u2R0vwKgRLdjyX8k8a+tAiBaurLLYqwVH6Kt9ZEEvrK1HixVjVQAFBVXTmwHlWPno2XilQiKWi2Rr7WlnwQVBNILQNSnooJ/FHzKxTn9m30M4NGUUAj3li9Jz/uY9Lz/w77/AAD+Xqdx8KZfRrqE9z6t+s/uUYlyLEmOPY+/djRRtrCqt3RWSZR1TY8sjaJqaIoWT5yrJcko5GTkUGgWNkuiLEQqWr58OW+88QYeT2VlSWlpKTfddFOrS7RX7VnPgq+fYWDOAJ48dwZOTeZvEEKkLkm0RWpIkEBaADYbVoJubs3aolEt+a/SEm9GKwCi4+0Vq0okVcpFEvyqFQFUvs9oRUBlDwBoil4AtnAAMjoBUtN/Isk/+BUHQwUMbqHZxrXCIjz//Bfef35M+r/+jVZWjmm3U9L/XApuHsX+H/UhdFKVLuFVJiSysKLrnkaSYV3T4y3IulqZNB+dKCtK9e1GxxxttRat27xly3CqKlOmTUt2KCkvFArhPKr3iMPhaHUVTa/uW8cT377Mpe0u4vFzH8Sutt6u70KIE4Mk2kLUJ1E38noqAKAZKwES9AJQAwGUYBDskmifSDZ9H13WK+f8Rp9rWSYhM4SJGX1e2YIc73ptWTi//Z6sjz4h86PNeP6Tj2KahHKyKb5sCL5LLqb8/H5obg85WTlkFhdXS5Rjkw0dT6IsBMAnX3yBTX5+GiQ3N5drrrmG/v374/V68fl8fPrpp4wdOzbZoTXY7/J/xxPfvsywkway8JzpLd5jRwghjoXcqYRorRJ1I5dl3U44SijMPw5vpk96L3IcWY061zDDVJgVeGwedEVHVaMtzYqCEgrj+uwL3JvycP1jE/revQCEzjiDsltvpWLIEMJnnAGqigOIdeT02D2E9Kad2EsI0Xg33HADl112GV988QVlZWV4PB4mTpxIu3btkh1ag7l1N7ecci0TT7tJkmwhRKshdyshxAnP5/OxceNGgsHK9SpHjEjN5bHqUlz6A1/48rmj+y8bdV7YCBGyQnRwd8CluwBQiopw5P0Dx8aN2P/5T1SfD8tuJ3jBBZSPH0/FoEGYHTo0x9sQQjSDLVu28NFHH+Hz+UhPTycQCPCzn/2s1fQqGd5jOP3LsmTYhxCiVZFEWwhxwrvrrrto3749nTp1AppmSaSW9n/f/QMLq1Hjs0NGEMMy6OjuiHvvARwffohj40Zsn3+OYpoYOTlUDB1KxeDBVFx4IbhczfgOhBDNYdasWZimyeDBg0lLS6OsrIyNGzeyadMm5s2bl+zwhBCizZJEWwhxwrMsi8cffzzZYRw7wyDv0L/IsWdxVvrpDTqlwgigoNAprRPuL7aSdfvtKIZBqFcvym65hYrBgwmfdZYMQxAp6eT27bHLz2aDfP3117z66qvV9l1++eWMGjUqSREJIcSJQRJtIcQJr3fv3nz++eeceeaZ8X32eia5SyWmv5yPirbwk/Y/jk84lkiFEUBTNNq726OrOu5XX8Xyejn86quYJ5/cAhELcXyemjcvso52sgNpBUzT5JNPPmHAgAHxfZs3b8ZmsyUxKiGEaPsk0RZCnPD+9a9/8be//S3+XFEUPvjggyRG1Dhf7f83JUYZgxrQbTxg+LGrdk5ynYSmaqj79uH4+98pu+kmSbKFaIMWLlzIggULmDJlCgCqqnLmmWcyZ86cJEcmhBBtmyTaQogT3tq1a7Esi4KCAjIzM9G01jXhzqaD/4euaFyc0z9hOX+4HJfmop27Xbzl2/3mm6Ao+K+/viVCFaJJPLxoEQ5N48GZM5MdSso79dRTeeaZZ5IdhhBCnHAk0RZCnPA+/vhjpk+fjtfrpaSkhDlz5jBw4MBkh9Vgm458Qt/0M/HqabUXsCz8YT8eu4dsZ3blZG9+P64//pGKyy6TWcRFq7I1P1/W0W5mpmkyc+ZMtm/fjt1uZ+7cuXTt2jV+fP369bz88stomkavXr2YOXMm4XCYadOm8f333+PxeJgxYwbdunVL3psQQogkapZEu76b89q1a1mxYgWqqjJy5EjGjBlDKBTigQceYO/evaiqypw5c+jZs2dzhCeEENUsWbKE119/nQ4dOvDDDz8wadKkVpNo7/XtJb/sGyZ3G19nmXKjnHR7OlnOrGozqrveeQe1pITy3NyWCFUIkQRjx44lFKq+pr1lWSiKwqpVq+o87/333ycYDLJ69Wq2bNnCwoUL4y3jgUCAJUuWsG7dOlwuF1OmTGHDhg3s378ft9vNm2++ya5du5gzZw4vvPBCs74/IYRIVc2SaCe6OQM89thjrF+/HrfbzfDhwxk+fDibN28mHA6zatUq8vLyWLJkCcuWLWuO8IQQohpN0+gQbdHt0KEDDocjyRE13IbvNwBwSXbt3cbDZgiH6iDblV39gGXhWrWKUO/ehH70o+YOUwiRJPfeey8PPfQQTz31VKOGxXz66acMGjQIgL59+7J169b4MbvdzqpVq3BFl/wLh8M4HA527NjB4MGDAejRowc7d+5swncihBCtS7Mk2oluzhCZ4be0tBRd1+O1qt27d8cwDEzTxOfzoevSq10I0TI8Hg8rV67k/PPPZ/PmzWRkZCQ7pAbrmt6VazoOo7urc63HDcsgzVazS7nt3//GtmMHxTNmgHTBFaLNOu+88/jFL37B9u3bGTZsWIPP8/l8eDye+HNN0wiHw+i6jqqqtGvXDoCVK1dSXl7OwIED2bt3Lxs2bGDo0KF8/vnn/PDDDxiGUW+Cr2kaOTk5tR7TdZ2cnBzKMrzYXGmoaup8PtR0jcwU/HshcTVeqsYWLCkkOycbpycz2aGIY9Asd6tEN2eA008/nZEjR+JyuRg2bBjp6emUlZWxd+9efv7zn1NYWMjy5cvrvU5DbsxWWRZ+M4g9Lb1p3lwTSNVf5lSNC1I3tlSNK6Aa6JpOZh2/H6K6RYsW8fTTT/Pkk0/Ss2dP5s+fn+yQGmzQKYPoXebGCodrPW5aJg6tZgu9+403MDMzCfzsZ80dohBNruepp8o62o0wYcKERp/j8XgoKyuLPzdNs1ojiGmaLFq0iN27d7Ns2TIURWHkyJHs3LmTcePG0a9fP84+++wGtaIbhsGRI0dqPZaTk8ORI0coLS5FqwijaKmTaGdmZFBUXJzsMGqQuBovVWOzE6bgSAGOCiNhuU6dOrVQRKIxmuVulejmvG3bNj788EM++OAD3G43U6dO5Z133mHLli1ccskl3HPPPezfv5/x48ezbt26hF04G3JjLiwsJFBShD1sNe2bPA6p+sucqnFB6saWqnEFfSVkGOE6fz9iTvQb84EDB+jYsSOHDx/mhhtuiO8vKChoVa3aiSgo6Er1W726fz+ODz+kfPx4cDqTFJkQx+7xhx+WdbQb6dtvv2Xbtm107tyZs88+G5/Px549ezjjjDNqLd+vXz82bNjAlVdeyZYtW+jVq1e14zNmzMBut/P000+jRis9vvzyS/r378/06dP58ssv+e6775r9fQkhRKpqlkQ70c3Z6/XidDpxOBxomkZ2djYlJSWkp6djs9kAyMjIIBwOYxiJa2+EEOJ4rFixgmnTpjFjxgwURcGyIhVyiqLwyiuvJDm6pmFioqnVW5Tcv/sdAOXXXZeMkIQQLciyLGbNmsWRI0e4+OKL+eKLL1i2bBmzZs1izpw5LFu2jOzs7BrnDRs2jLy8PEaNGoVlWcyfP59169ZRXl5Onz59WLNmDQMGDGD8+MhEjOPGjaN///785je/4cUXX8Tr9TJv3ryWfrtCCJEymiXRTnRzzs3NJTc3lzFjxmCz2Tj11FO55pprCIVCTJ8+PT4D+eTJk3G73c0RnhBCADBt2jQgMsYwZv/+/W2npd+yUBUVveqYxkAA11tvUXHppZht5X2K1ilasVXvYy377l2wALuuM++xx1og0Nbt9ddfJyMjg1tvvZUuXboA8Pnnn7N48WIeeOABnnrqKR5++OEa56mqyuzZs6vtq7oazLZt22q93ksvvdR0wQshRCvWLIl2fTfn0aNHM3r06GrH7XY7v/nNb5ojHCGESOiVV17B6XRSUlLCH/7wBwYNGhRPwluzsBnGrtir7XO++y5qcTHlR92DRStmWceUrNZWRqntPJsNpcpwsEjBygn0LKvm0CzFsuqfZE9RIl/RbsdWrHzsUVVBUSL7Y+Oxo4879+3DZrNhRXvCibr9+c9/5oUXXuC2225j165dnHfeeZx77rls3bqVc845p1XNSSGEEK1J6swoIYQQSfL222+zcuVKJkyYwNtvvx3vCtnahQnj1b2VOywL96pVhE47jVC/fskLrC2wLAiHITbE6ahkU6ma/NZFUSJlYsMWbDY4KqFVYq+dKGmtkoTWmqwCVtVENbZ9VJn4+VUTW0WB7GzM6DJOVtXzEj02pMzxsNkqv0RCmqbhdDpp374906ZNw+Vy8dxzz3HRRRcByCovQgjRTOTuKoQ44SmKwqFDh2jXrh2KolCcghPcHQvLsnDolRNK2j77DFt+PiUPPSRLejWWaUIohBIOYxH5mTFdLogmoEe3uFZLbI9KLuPp99GJaU4Optdb81jVMrUda24eD1ZFRctcSzS52ERle/bs4ayzzgJgwYIF3HbbbUDtPRKEEEIcv4SJdtW1D8vKynA4HFLzKYRocy688EJuvPFGFi9ezPz587niiiuSHVKT0aicCM29ahVmejr+n/88iRG1EoYBwSBKrMVa0zDT0rDc7kh3Zbu96RNdhyPyukI0IafTycGDB7n44ou59957GTduHOXl5Rw5cgS/3y+f64QQopnUeXfNz89n4sSJrFmzhoyMDP75z3+ycOFCli9fzmmnndaSMQohRLOaPHkykydPBuCcc86Jr4DQFsQmQlMPHMCxYQPlv/xlZSusqBQKQTgc7/Jt2WxYXm9lYt2GfibagrPPPhunLE3XIDfffDNz587lySefZP369SxbtoyMjAwWLVrECy+8wFVXXZXsEIUQok2qM9GeN28eTzzxRHwt2aFDh5Kdnc3cuXNlRkkhRJswe/ZsZsyYQW5uLspRrZOrVq1KUlRNw7JMFEWJL+3lWrMGLIvyKuuFn7AsK9INPBSq3OVwYGVmYjqdkVZlTUvwAiLZZs+eTU5ODkeOHEl2KCnvwgsvZN++fUyYMIHc3FweeOABSkpKWLlyJWVlZUyaNCnZIQohRJtUZ6JtmibnnHNOtX39+vUjVOWDiRBCtGZ33XUXAE888QSWZaEoCsFgEHsb6L5rmAYONTo+u6IC9x/+QMXgwZgnn5zcwJKhyvhqbDYUvx/T5cLKzMSy2yOJdWxMtRBt0DXXXMOgQYN45513+Pjjj3G5XAwbNoyBAwcmOzQhhGizEibatQmHw80WjBBCtKR27doBkJeXx44dO5g+fTq33HIL//M//8Mpp5yS5OiOT5gwbs0NgPO991CLiigfNSrJUbWQcDiSWMf+juk6ptuN5XZDx44YJSUyGVwrN2nSJBwOB4sXL052KK1Gu3btuPLKK6mQie2EEKJF1JloDx48mEcffZS77roLr9dLWVkZv/3tb+PLQQghRFvxxhtvxLuKP/vss9x4442MGDGizvKmaTJz5ky2b9+O3W5n7ty5dO3aNX58xYoVrFmzhuzsbABmzZpFjx49GDFiBN7orNKdO3dmwYIFzfaeLMvCrtvjS3qFe/YkdP75zXa9pIkus6WEQvH1pC27HSs9Hcvlqjm+ujkmMRMtbv/+/W1qLoWWMHPmTDZu3Ej79u3jPXha+xAZIYRIZXUm2rfffjvPP/8811xzDYFAgIyMDEaMGMGtt97akvEJIUSzU1UVhyPSzdpms9UYr320999/n2AwyOrVq9myZQsLFy7kmWeeiR//6quvePTRR+nTp098X6wVaeXKlc3wDmqnKzq2zz/Htm0bJdOnt40EMza+OhyOJAuA5XRiZmdjxWbtlvHVQtTwxRdf8P7778eX+xJCCNG86ky0FUXh9ttv5/bbb4/XfAohRFt0+eWXM2bMGM4991y++uorLrvssoTlP/30UwYNGgRA37592bp1a7XjX331Fc899xyHDh3i0ksv5Y477mDbtm34/X5uueUWwuEwU6ZMoW/fvs32niwsNEWLLOnl8eAfPrzZrtWsDCOSWBtG5G+Rqka6gcv4aiEapWvXrlRUVOCSVQeEEKJFJFw88cUXX2T16tX4/X5sNhtjxoyRFm0hRJtz11138ZOf/ITdu3czYsQIzjjjjITlfT4fHo8n/lzTNMLhcHw92uHDhzNmzBg8Hg+TJk1iw4YNnHzyydx6661cf/31fPPNN9x22228++67zbKGrWWZaIqGpqjYP/qIwLBhrWdJr6rjqy0LbLbq61fbbG2jZV6IFrZ//35+8pOfxIe5SNdxIYRoXnV+wnvppZfYvXs3v//97/F4PPh8PubPn8///u//MmHChJaMUQghmtUPP/zACy+8QGFhIT/96U+pqKjgvPPOq7O8x+OhrKws/tw0zXjCbFkW48ePj4/FHjJkCP/5z38YOHAgXbt2RVEUunfvTmZmJocOHaJTp04JY9M0jZycnFqP6bpOTk4OwcwswuEK7I40AIJmkCw1i6yiclSfD/uAAWRlZTXqe3K8dF2v/5rRbuCEQpGZwRUFPB5ISwO3O9Ja3cQVEbHvWSpK1dhSMa5LLrkEVVVTLq5UJhPHCSFEy6rzE8x7773Ha6+9Fh/L4/F4mDVrFjfeeKMk2kKINuXhhx/m5ptv5umnn2bAgAE88MADvPnmm3WW79evHxs2bODKK69ky5Yt9OrVK37M5/Nx1VVX8ec//xm3283HH3/MyJEjWbNmDfn5+cycOZMffvgBn8/HSSedVG9shmHUuVZwbB3hoqJCrHAYzRlZFSJg+MmwZ1D+r39jB4o7dyZcWNi4b8pxysrKovDoa5pmZOKycBjLjKzzbTmdWGlpWC5X9fHVfn/kq4ml8trLqRpbKsY1efLkBsdVX2XWiULTNObPn8/OnTvp1q0b06ZNS3ZIQgjRptWZaNtsthoTZthstmbp5iiEEMlUUVHBxRdfzDPPPEOPHj3iE6PVZdiwYeTl5TFq1Cgsy2L+/PmsW7eO8vJycnNzmTx5MuPGjcNut3PxxRczZMgQgsEg06ZNY/To0SiKwvz585vtfmpZFnbNjp6fj6UohE87rVmuU6+q46uhcnx1VlZkfLXNJuOrhWghDz30EKNHj+b888/nX//6Fw8++CAvv/xyssMSQog2K+FkaEeOHKnWLevw4cMyW6UQos2x2+384x//wDRNtmzZgt1uT1heVVVmz55dbV/Pnj3j2yNGjKixPJjdbm+xrpuxidD0/HyMrl1bbnx21fHVNhtKOIzp8VRfZkvGV4smMGHCBOx2O08//XSyQ2k1KioquPzyywEYOnQoK1asSHJEQgjRttWZaN95553cdttt/L//9/849dRT2bNnD8888wxTpkxpyfiEEKLZzZkzh0cffZTCwkJefPFFZs6cmeyQjouCgqZo2LZvJ1RlibEmVXX96uhzy27HysjAdDqhUyeM4uLmubY44RUWFso62o1kGAbbt2+nd+/ebN++XVaTEUKIZlZnon3RRRfx6KOPsmrVKtasWUPHjh2ZM2cOZ511VkvGJ4QQzW7FihU8+eSTyQ6jScRmHNfLytH27aP82mub5oVNs/r61aoaWb86PT3SDdzhqN4NXIYZCZFSHnroIaZPn87Bgwfp0KEDc+bMSXZIQgjRpiX8JHT66afz8MMPV9v397//nSFDhjRrUEII0ZJ27txJSUkJ6enpyQ7luIXNMHbNgf7f/Mjz3r2P7YWqjK/GskDTIuOr3e7K9aulRUyIVuOss87i97//fYPLm6bJzJkz2b59O3a7nblz58aXBgNYv349L7/8Mpqm0atXL2bOnIlhGDzwwAPs3bsXVVWZM2dOtWE1QghxIqkz0f7DH/7AE088gdPpZOnSpXTp0oWHHnqIXbt2tZpE++uir5n96cN0t7XngpMGcG7mWbg1Z7LDEkKkmJ07d3LhhReSlZUVn4di06ZNSY7q2BgYeHUHen400a4yI3pCpgkVFZHx1YCl61geT+X61bouibUQrdDdd9/N0qVLueSSS2ocS3Sfe//99wkGg6xevZotW7awcOFCnnnmGQACgQBLlixh3bp1uFwupkyZwoYNG7Asi3A4zKpVq8jLy2PJkiUsW7as2d6bEEKksjoT7RUrVvD2229z6NAhFi5cyMGDB7n88st5/PHH633R+mpB165dy4oVK1BVlZEjRzJmzBj+8Ic/8NZbbwGRCTv++9//kpeXd1wtTOnbdrF03ma+TgvwZfvf85uOCoHu3ck5/Uf86KS+9M08G6+edsyvL4RoGzZs2JDsEJqMaZnYVBu27dsxs7Iw27Vr0HmK34+ZkYHZTOtXC9GULrnkEtxud7LDaBWWLl0KNL7y8NNPP2XQoEEA9O3bl61bt8aP2e12Vq1ahSs60WI4HMbhcNCpUycMw8A0TXw+n6xUI4Q4odV5B8zMzCQjI4OMjAx27tzJzJkzG9ySnagWFOCxxx5j/fr1uN1uhg8fzvDhw7n22mu5NjqWcNasWYwcOfK4u3F26nouzrN+TNf/buPqvAOopgnsIqjtYnvO7/mqPezpkknxaafCGWfT6ZSz6eXpTntHjkwSIsQJ5N///jezZs07WXL0AAAgAElEQVTiyJEjtG/fnnnz5nHmmWcmO6xjEpsITc/PJ9S7d8NboS0LKyMjMjO4ECmuMetoi4jNmzfj9/uxLIs5c+bwq1/9iquvvrrO8j6fD4/HE3+uaRrhcBhd11FVlXbRSryVK1dSXl7OwIEDOXDgAHv37uXnP/85hYWFLF++vEGxaZpWbZWbqnRdJycnh7IMLzZXGqqaOsm7pmtkZmQkO4waGhqXFfvPsjAtM/4cK7JMZOzY0ap+RlZQat1f86RIjyuX11n9nATn13Ws6v7anh+LVP23DJYUkp2TjdOTmexQxDFIuLxXzMknn9yo7uKJakEBevfuTWlpKbquRybVqXKtL7/8kh07dvDII480+Hp1MTt1Ys/cGQSKDuLQXTi+24Nj1zfoO3eS/vVXDN31He22FgFFwBfs8cJnneCdk20c6nEyod69yOp6Fqd5u9POnkW6zYtHc0sSLkQbM3fuXBYvXsxpp51Gfn4+M2bMYNWqVckO65jpBug7d1I+enTDTjCMSIItSbYQbdaiRYt4/PHHmTVrFm+88Qa//vWvEybaHo+HsrKy+HPTNKu1UJumyaJFi9i9ezfLli1DURReeuklLrnkEu655x7279/P+PHjWbduHQ6HI2FshmHUWWkSq1ApLS5FqwijaKmTaGdmZFDUgqsrWJZZmQBHk+Cq/0X+t0jPSKe4uBiVyFCo2PEaCaqioKLGHzVVQ0FBVVQ0RUNV1fhrxGPAqhJPJEGveo2qx2JiZTIzMykt9UGVcmZ0uBKASeV21WsdnexXLVdbRUBtqr7/2r4X6enplJSU1HkO1J3QW5ZF7FBtr12XauXqOMWrKhQcKcBRYSR8rU6dOjXomqJl1Xm3KioqIi8vL979p2qXo9rG+VSVqBYUIpOsjRw5EpfLxbBhw6q1XD/77LNMnDjxmN9QVb/85S8JBcpYMuteLLudwGk9CJzWA7gMgBLgh1Ifrq93omz7CvM/X3LBjm8Y/vcC1A+/Bb7liOuvbOkIe72wzQUFboUyj52Ax0WF100ow4uZng6ZWdjTMki3e/HqHtJtHtJ1D+k2b/TRg0dPQ1O0JnlvQoim4/V6Oe200wDo1asXTmfrnMvBNA00RcP+7bcooVDDx2eHQphV7tlCpLpf/vKX2Gw2XnrppWSH0mo4HA5ycnLQdZ2TTjqJYDCYsHy/fv3YsGEDV155JVu2bKHXUfeTGTNmxNcyj81tkZ6eHl92LSMjg3A4jGEkThDarGgSHGspjiXBJmZlQmpVT1xrvMTRiZ4S6bGkEE2MNQ1VqZ4oq4pKVloWzlCk5VhBQVHqfmxJWWlZ2ILNV6F7dNJdV+Jf2/c8Kz2LQrOwznIJX7uOf8Oj99dW+VBb2aqVD5k2G0FVcofWqs5E++yzz2b9+vVAZKbKt99+O36svkQ7US3otm3b+PDDD/nggw9wu91MnTqVd955h5///OeUlJSwa9cuLrroogYFX19XI8MwMAyTzPRM7Gl1dEPPyIDOp8BPBgMQBPb5A9i+3oH+3+2EvvyMc7bnM+BAMY7Scpz+EFAR/Sqq9lJBDY64oMAVeTzijmx/F90+4gK/x0EwPY1QhhcrIx0rMwt3WgYZNi8Zdm/0MT3+PN1Wuc/WzN2lUrXbDKRubKkaV0A10DWdzDp+P0R1OTk5PPjgg1x00UV89dVXmKbJ6tWrAcjNzU1ydA1nWAYOzYGe/zlApOt4AyiGgSXjXUUrEggETtwE7hilpaVx8803M2bMGF577bV6W8CGDRtGXl4eo0aNwrIs5s+fz7p16ygvL6dPnz6sWbOGAQMGMH78eADGjRvHTTfdxPTp0xkzZgyhUIjJkyc3+1h6paKCv994O17DYGjutYRzsgnnZBPKySbcLjsy70QDVGstPqqluGqiDJFHFZWysI4/XF79daLJcSz5VZVIq7CqqfGhPaqioqrR59FWZCDemlw1CT76eUOk2dII6okrUtqiRF3P62tk1jUdPYWGJcRk2WwcScG4RMPU+S+3YMGCY37RRLWgXq8Xp9OJw+FA0zSys7PjXTU2b97Mj3/84wZfp76uRqFQiFA4RFFJEfZww7qWxHXtEvn62dBqu5VQCK3Uh1ZcglZSglZSil5cGtkuLkErLuKkokLaFxejlZZiP+jDWVqOHop9IIgl6QXx1yyzRxLyw0cl6N9W2T7iAp/HTsjrJpyRjun14okm4unxFnQvXltajZb0dN2LQ7PX+5ZbugtUY6RqbKkaV9BXQoYRrnf8onQ1iujRowcA3377LR6PhwsuuIBDhw4lOarGMywDR3TGcctux6gyCWVClhVZsksI0WYtXbqU7777Lj5E5vrrr09YXlVVZs+eXW1f1aW6tm3bVut5v/nNb44/2AZy7P6WLjMX0Gf/AYKKgn3ZszXKGC4n4ZxsgtlZhLLSMZ0OTLsdw+nActgx7Q5Mpx3L4cRyOrAcTnA4wekElwvL4UBxulFcrsg+Z+S4oulke7NJC6ehKpEW/WS2FoskMk0IBlGiX8e0XVGBEgrV2K916QLTpiX7HYpj1CxVJIlqQXNzc8nNzWXMmDHYbDZOPfVUrrnmGgB2795N586dmyOkJmPZbISzswhnZzXiJAulogK9OJKYZxgm/n370aOJulZcgqekFG9xMT2Ki1CLS7B9X4rdV45qVq0gCEa/Ii3pxS41kqC74ZDTjCTkbjjoinRzj7WiF7ig1KMT9HpRPdEkPJaY62nx7Y7pJ6GHNLw2L+lV9rs0p/zBEG3apEmTkh1Ck7CwsKk29O3bCffs2bCZww2jcvkuIUSb87vf/Y7rr78+Po66qilTpiQpquNkWWStfYeTly7HdDn5VY9ufOT18vL8GehHCtCPFGA7UoB2+DDqkcPYDhfgKCzB891+1EBFJKmpqEAJBFCOsWeEZbOB04nb4cByOiH6aDkckS+ns3J/1WP17LccjsrXij232WR5xdqEwxAKxZNUpaKi9mQ2FIosX5kg4dUUBW9p6bEly+Fwk7wdy2aL/3tbDgeWzRZ5T6LVapZPVvXVgo4ePZrRtUzSM2HChOYIJ/kUBcvpJOR0EurQHmdGBiUNaQU1TdSy8moJuV5SGt/WSkrIKimlXXEJZ5WUoB4uRi8pxVbmr+XFwkAhYbWIErdGYZrCYRccdpocdBqRZL1Ki3q8C7wbit0qdlek9bxqYl45Dt0TGZdepSXda/NgU3R0RYvMgKxGH6PPY7W/QoimpaNhy8+n4tJLG3ZCMIh1nCs8CCFSV8eOHYHKnjutnVpSSufHfkPGh/+g9Px+7HloKh89EumFaWSkE073UNG1A6Zlois6GY4MXLqLsKpTVtsLxhK1QCCSjMUS8ECgcruiAqrui+53AhXFxTX2q6WlcOhQtfNjX8fCUtVaE/C6EnMtI4M0qFn+6EqBOioGUBN8RrMsCIerJ5oVFdUSXqomvrH9wSCqruMuKqq7zFHJctWkNv686jlNMHzEUpTIkpYOBw6bDex2rOhXfNvlwsrIqLm/Kbdttlq/71kySWmrljDR3rlzZzxB/v777/H7/TUmw0hlQ4cOpby49XX/jFNVTK+HoNcDpzTivLCBVloaSdBj3dpLSqMt6pHtdsWldIh1dy+IJu4VdY3nMamwl1KSFqDIfSTazd3ioNPgB2eIQy745qhu7gUuCOhQoUfGrh89NkYlMqOlpqjYVD26XZmI64qGXmW/Q7ejmFSWU6uW1SvPUTS0Kkl9rEysXOx4tesoejwWXdWPOqdKWVWvsS9LzaTc70dX1JrXicYZm9FTiJZgO1KAWlREqIH3asU0MaNr4QrRWgwdOlTW0W6g2Cow3bt354svvmDcuHHcc8893HLLLUmOrPHcW//DqbMXYTt8hP133srh0deBqjL4xxdgWAb+UDmKouCxeUizpWHX7PX//bXZIi2JxzAhpC0ri9LCwoafYJrVk/nYtt9fc380sa9rv1JRETkWCKCUlFSvHAgGSfP7UUyz/phqYdnt8QQcXa+ZCB8Hb+wamlZ/EurxYDoczZPYxh51HRSFrKwsChvzbylEA9SZaL/33ns88cQTrFmzBq/Xy6FDh5g2bRpTp05l6NChdZ2WUu68804K9uQTKDqY7FBalq5hZGViZDVuzb0sh4PSPXurJeRacWm1FvUOJSWcXFyKVlqKtifSwt6QGsWQTSOsq4R1jZBNJWRTCesqIV0lqKsEbUpkW1MI6lChGwR1k6CuENAswnaVctUioFlUaBDQLfyaRUCP7PNrFn7NxK9ZlGkm5ZpJuWpSrhuUqwblmkG5alGhg5mExvRECX2NxF6tUk6pWS5SyVC1ckKtfB6tREgzdX7dsxdOu7RUNoTP5+P555/n0KFDXHrppfTu3ZuuDR3fnCIMDHTVgT3/a4CGzzgOkdp8IVqRO++8U9bRbqS5c+eycOFCAH7961/zwAMP8NprryU5qgYyDDq8/ns6vbqaYIcO7HzmCfxnnRE/nHvDcByagwxHBg7Nkbq95lQ1Mu7b5Uow1/jxy8rKorCgINLyXCVBp0piX7UFnqMS+Gr7w+FaW19jXZuJPlpHJcS1JbYZ7dtTWF4eab2V4UriBFDnT/mLL77I6tWr8XojdU/9+vXj9ddf584772w1ibZoHMvpJHxSO8IntWvESRZqeXmNhFwr9aFWGRujBEOooVC0+08IRyiEs8pzNVpGCUT2xcuGQpHXCYWbpIsQgKlpWDYbpl3HjD4aNhumrmHYdAybRtimY9h0wrEKApsW3dYI6QohW6TCQHHZ8ZkGIV0haFMI6pGvCk2hQo9VGlgENKjQFSp0CKgmfh0qNIuAamJgYlgGITOMYRkYlknYChO2DAzLIGgGI9umQdgyCFu1lwub0UfLwKnaGe0fS9e0jk3yPWvrpk+fzuDBg9m8eTPt2rXjwQcf5NVXX012WI1iWiYu1Y6enw9A+PTT6z8p9gFKPvAI0ebpuh5fxrBLly7xJblaA8/SpXhffoPCy4ew775fYaalxY8FDD821UZ7d/vUTbCTQVEqW+u93vrLt4TMzEjXcyFOEHV+urLb7WRmVm8RzcnJweFwNHtQTWXkyJGEKspZvvDBZIfSdikKZloaZloaoZObL6nLzMigqKCgMvEOhiIJfCiEWhGMbx+d0Kvx/Ucl9LFkPvY8+rp6MIQt9rplgcqKglAINVilbCjUJO/LUpTIH0G7DTP2B9Fur3xut2HZI7XF8ec2G2a0jGWr/XnAqeFN64IsftMwRUVFXHfddaxdu5Z+/frVWC+zNQibYZy6E1t+PuHOnRv2wSoUwkrB5emEqM/IkSOx2WysWrUq2aG0GieffDJPPPEEffv25YsvvqB9+/bJDqnBAsOHczDdRtmgH6PolWNWw0YIBYW7fj0DRVF46/nnkxilEEJUV2eirSgKgUAAp9MZ3+f3+wk1UYIhRKNpGpamYVT5mUwa00QJhcl0uSg5ciTa6h5L4kOoodiEHbFkP1jlWKiOBD7agl9RpRIhVkFQVo4eKxs9v+q2clRiaKkKBQMvxTjv/CR9g1qfnTt3AnDgwIFW1dITY0FkSML27Q1rzQYUy8JMhd8nIUSzW7BgAW+88QYbN26kZ8+e3HXXXckOqcHCvXpRUnEYrcp4a8MME7JCdErrJPOgCCFSUp2J9rhx47jtttsYP348Xbp04cCBA/zv//4vN954Y0vGJ0RqUlUshx3L68EwjeS2HFsWGEZl4h0KEQyU0unM3smMqlV58MEHmT59Ojt37uTuu+/mkUceSXZIjaYANn8Q7fvvCVx5ZcNOsqzIDLNCiDZP13XS0tLIzs6mV69e+Hw+srOzkx3WMbEsk4ARoGNaR+yazDEhhEhNdSbaQ4cOJTs7m9/97nccPHiQU045hXvuuYe+ffu2ZHxCiPooCug6pq4Dkdmjw77W1yKbTL1792b16tXJDuO4aKqGc9c3KJZFuHcDKllCocj4bE1r/uCEEEk3Y8YM2rdvz0cffUSfPn24//77eb41drW2LPyGnxxXDi5dVkwQQqSuhDPg9OvXj379+rVULEIIkRSDBg2ioKCArKwsioqKsNvttGvXjkceeYSBAwcmO7wGsak2bNGJ0EINnQgts3ErEwghWq/vvvuOefPm8cknn3DZZZfx3HPPJTukY1JulJNuSyddVtUQQqS4OhPtSy65JL6tKAqGYdCzZ0/mzJlDt27dWiK243b11VdTfqIt7SWEaLTzzz+fSZMm0aNHD7777jt++9vfMnHiRKZOndpqEm2H5kTPz8f0ejE7daq3vGKaMj5btFpXX301aVVmnhb1MwyDgoICFEXB5/O1yrkoAkYAty2NLGdWtf3/M2xYkiISQoi61Zlob9q0qca+Tz75hFmzZrFixYpmDaqp3HTTTSfmOtpCiEY5cOAAPXr0AODUU09l//79dO3aFa2VdKtWFAW7ao9MhNa7d2Q4QUPI+GzRSt10002yjnYjTZ48mdGjR3Po0CFyc3N58MHWtyKLrujkuHJqTH528w03JCkiIYSoW6MWTx0wYECrmnW8vLwcfyCAzEUphEjkpJNO4vHHH+dHP/oRn332Ge3atSMvLw+bzVb/ySlAVTR0S8G2Ywfl11xT/wmhEJbDAa2wRUsIiPx9d7lkfG5j7N+/n/feey8+TKa1zdTttrtJc2ehqTUrQMv9/kgZ+ZkQQqSQRn/K8vl8zRFHsxg7dix3/vr+ZIchhEhxjz32GO3bt2fjxo106tSJhQsX4na7eeKJJ5IdWoM4dQeufT+gBAINmghNCYWwPJ4WiEyI5jF27Fh+8YtfJDuMVuXNN98EIDs7u9Ul2QBZzixsWu2Vn7+8+25+effdLRyREEIk1uCu48FgkL/85S/079+/2YMSQoiWpGka55xzDmeeeSaWZfHXv/6Vq666KtlhNZjX5sWx4yOAhs04bllYMj5biBNKMBhkxIgRdO/ePT4+e/HixUmOSggh2q46E+2333672nOn00mvXr3wR7vnCCFEWzFp0iRCoRAHDx7EMAzat2/fqhJtAFt+PpauE+7ePXFBy4o82mXtWSFOJPfee2+yQxBCiBNKnYn2ggUL4ttffPEFr776Ki+88AI//elPWyQwIYRoKT6fj1dffZUHH3yQhx9+mJtvvjnZITWa/vXXkSS7vgQ6HI60Zsv4bCFOCIcOHeLFF1/E7XZz66234na7kx2SEEKcEOpMtIPBIG+//Tavv/46NpsNn8/HBx98gFO6Gwoh2pjY7OJ+vx+n09mqJn2M0b/+muBFF9VbTgkGMU86qQUiEkKkgvvvv59hw4ZRXFzMokWLeOSRR5IdkhBCnBDqTLQvu+wyrrrqKhYtWkS3bt2YMGFCq0uyb7jhBsoKDyQ7DCFEirviiit46qmnOOOMM7jhhhvwtLKJwpSCArTDhwn36lVvWQuwpNu4aOVa4+9psoTDYUaPHg1ElkVrKNM0mTlzJtu3b8dutzN37ly6du0aP75+/XpefvllNE2jV69ezJw5kz/+8Y+89dZbAFRUVPDf//6XvLw80tPTm/Q9HS336qub9fWFEOJY1Jlojxs3jvXr17N3716uu+46rNi4vlYkNzdX1tEWQtSrZ8+eXHjhhSiKwpAhQ6p9mGwNbF9/DUCovkTbsiLLHcr62aKVy83NlXW0G6jqDOOmaTb4vPfff59gMMjq1avZsmULCxcu5JlnngEgEAiwZMkS1q1bh8vlYsqUKWzYsIFrr72Wa6+9FoBZs2YxcuTIZk+yAUb9z/80+zWEEKKx6hykd/vtt7N27VrGjh3L+vXr2bp1K4sWLSI/P78l4zsuR44cobCoKNlhCCFS3LJly+IfRnv37t3qeu/o0ftyvS3aoRCm2w2tcGkfIao6cuQIhw8fTnYYrYLf7+ebb75h165dBAIBvvnmG3bv3s3u3bsTnvfpp58yaNAgAPr27cvWrVvjx+x2O6tWrYqvZR4Oh3FUqcD78ssv2bFjB7m5uc3wjmo6UljIkcLCFrmWEEI0VJ0t2jEXXHABF1xwASUlJfzpT3/ivvvu449//GNLxHbcbr/9dkIV5Sxf+GCyQxFCpDBFUZg4cWK1ZW+mTJlSZ/n6ulSuWLGCNWvWkJ2dDURadrp165bwnOOhb9+O0b49VmZmwnJKOFxvGSFag9tvvx2bzcaqVauSHUrKczgcPPzwwzW2FUXhlVdeqfM8n89XrXu+pmmEw2F0XUdVVdq1awfAypUrKS8vZ+DAgfGyzz77LBMnTmyOt1OrCffdB8Bbzz/fYtcUQoj61Jtox6SnpzN27FjGjh1bb9n6PoSuXbuWFStWoKoqI0eOZMyYMUDkxvy3v/2NUCjE6NGjuf7664/hLQkhROOMHDmyUeUTdakE+Oqrr3j00Ufp06dPfN9f/vKXhOccD9vXXzdsfLZpYkm3cSFOKCtXrjym8zweD2VlZfHnpmmi63q154sWLWL37t3VegWVlJSwa9cuLmrA5IwxmqaRk5NT6zFd1yPHiosjqyrUsmKCLRpXVlZWg6/ZFHRdb/FrNoTE1XipGpseCER+/ltZTzsR0eBEuzHq+xD62GOPsX79etxuN8OHD2f48OFs27aNzz77jDfeeAO/38+LL77YHKEJIUQNV199NW+99Rb79+/nwgsv5PTTT09YPlGXSogk2s899xyHDh3i0ksv5Y477qj3nGPm96N9+y2BIUMSl7MsFFWV9bOFEA3Sr18/NmzYwJVXXsmWLVvodVRl3owZM7Db7Tz99NPxnkAAmzdv5sc//nGjrmUYRp3j7WNj8bWiIiybrdZEOxQOA1DYwt3Hs7KyWvyaDSFxNV6qxpZls0V+N+pJtDt16tRCEYnGaJZEu74PlL1796a0tBRd17EsC0VR2LRpE7169WLixIn4fD7ui3YDEkKI5vbII4/Qvn17PvroI/r06cP999/P8wm6ICbqUgkwfPhwxowZg8fjYdKkSWzYsKHec46VLT8fxTAI11M5IOOzhRCNMWzYMPLy8hg1ahSWZTF//nzWrVtHeXk5ffr0Yc2aNQwYMIDx48cDkUl0hw0bxu7du+ncuXOSoxdCiORrlkS7vg+Up59+OiNHjsTlcjFs2DDS09MpLCxk3759LF++nD179nDnnXfy7rvvVpst82j1dTWy2Wxg2MhMz8Se1vyzXjaUpmtkZmQkO4waUjUuSN3YUjWugGqgazqZdfx+iOq+++475s2bxyeffMJll13Gc889l7B8oi6VlmUxfvx4vF4vAEOGDOE///lPvd0w61LffS7zm28AcA8YgDtRtzefDzp2hBaYATgWW11xJ1OqxgWpG1sqxmWz2VAUJeXiaktUVWX27NnV9vXs2TO+vW3btlrPmzBhQrPGJYQQrUWzJNqJPlBu27aNDz/8kA8++AC3283UqVN55513yMzMpEePHtjtdnr06IHD4aCgoCDhH9H6uhqNHj0aX8F+ikqKsIdTZ3myzIwMioqLkx1GDakaF6RubKkaV9BXQoYRrnfpG+lqFGEYBgUFBSiKgs/nq9YNsjaJulT6fD6uuuoq/vznP+N2u/n4448ZOXIkgUAgYTfMRLElus/5jxzB06kThV4vJOr25vNher0QCjXouscrVZdeStW4IHVjS8W4Ro8ejdfrbVBcJ/p97pJLLgEgFArh9/vp1KkTBw4cICcnh7/97W9Jjq5pjL/uumSHIIQQNTRLop3oQ6jX68XpdOJwONA0jezsbEpKSujfvz+vvPIKN998MwcPHsTv95N5nLPj/uIXv5B1tIUQ9Zo8eTKjR4/m0KFD5Obm8uCDiVcqSNSlMjc3l8mTJzNu3DjsdjsXX3wxQ4YMwTTNGuc0hfKbb46Mz05UOWCaKJoGNluTXFOIZPvFL36RkhUAqWjTpk0A3Hvvvdxzzz106tSJH374gQULFiQ5sqYz4qc/TXYIQghRQ7Mk2vV9CM3NzWXMmDHYbDZOPfVUrrnmGux2O5s3b+a6667DsixmzJiBpmnHFcfevXsp+uEgWTLJrhAiAa/Xy3vvvUdBQQFZWVkJh6xA/V0qR4wYwYgRI+o9p0koSmSSFNOsu4yMzxZtzN69eykvL8ftdic7lFZjz5498db9Dh06sH///iRH1HT2HjgAwCkdOyY5EiGEqNQsiXZ9H0JHjx7N6NGja5zX1BOg3X333bKOthCiXkuWLKGoqIhrr72W4cOHk5aWluyQmpQSCmFF17wVoi24++67ZR3tRurZsydTp07l3HPPZcuWLfTv3z/ZITWZSdG1wWUdbSFEKmmWRFsIIVqT5cuXc+jQIf70pz9x66230rNnT+bNm5fssJqMpSiRZXGEECesqVOnsmXLFr7++muuvPJKLr/88mSHJIQQbZok2kIIAYTDYYLBIKZpHvewlZRimrJ+thCCO++8kzfeeINLL7002aEIIcQJQRJtIcQJb/z48VRUVHDdddexdOlS1q5dm+yQmo5hYDlkogohTnQZGRm8/PLLdO/ePb6yQmxGciGEEE1PEm0hxAlv+vTpVFRU8Oqrr7JkyRKuuOKKZIfUdAwDy+VKdhRCiCTLyspi27Zt1da/lkRbCCGaT5tOtO+44w58R/YlOwwhRIoKBoO8/fbbvPbaa9jtdnw+H++//z5OpzPZoTUZxTCwpNu4aGPuuOMOvF5vssNoVY5ezuvgwbaz9OmdN96Y7BCEEKKGNp1oX3HFFbKOthCiTpdddhlXXXUVjz/+ON26dWPChAltKsmOsfQ2fasXJ6ArrrhC1tFupKVLl/L6668TCoUIBAJ069aNt99+O9lhNYkrhgxJdghCCFGDmuwAmtOOHTvY/e13yQ5DCJGixo0bx0cffcTixYv5+9//jmVZyQ6pyVmWBW1pcjchiPx93759e7LDaFU2btzIxo0bufrqq/nzn/9Mhw4dkh1Sk9nxzTfs+OabZIchhBDVtOlE+/7772fWgsXJDkMIkaJuv/121q5dy9ixY1m/fj1bt0fkQxQAACAASURBVG5l0aJF5OfnJzu0JqMoCkiLtmhj7r//fiZNmpTsMFqVzMxM7HY7ZWVldO3aFb/fn+yQmszUefOY2oaWZBRCtA1tOtEWQoiGuOCCC1i0aBF//etf6dixI/fdd1+yQ2oalgWKIi3aQgg6duzImjVrcLlcLF68GJ/Pl+yQhBCiTZNmDiGEiEpPT2fs2LGMHTs22aE0DcPAstmSHYUQIgXMnj2b/fv387Of/Yy33nqLJ598MtkhCSFEmyYt2kII0VbJjONCCGD16tWYpskpp5zCtm3b0HWd0047LdlhCSFEmyaJthBCtFWmCQ5HsqMQQiTRsmXLyMvLIxQKAZEu5Hl5eTz11FNJjkwIIdq2Nt11/Fe/+hWlh/ckOwwhhEgKRbqOizbqV7/6Fenp6ckOo1XYuHEjb775ZmRiRKBz5848+eSTjBo1iokTJyY5uqYxecKEZIcghBA1tOlEe/DgwbKOthDixKWqWDIRmmiDBg8eLOtoN5Db7Y4n2TE2m420tLQkRdT0Bl94YbJDEEKIGtp0or1161ZKDn5Lt/YZtR5XQiHUiiCWGvkDZGla5EOppkVm6hVCiNbMNGXGcdEmbd26lczMTDp37pzsUFKe0+nk+++/p0uXLvF933//fY3k+2imaTJz5ky2b9+O3W5n7ty5dO3aNX58/fr1vPzyy2iaRq9evZg5cyaqqvLss8/yt7/9jVAoxOjRo7n++uub7b3FbI2uqd6nd+9mv5YQQjRUm060H3nkEUIV5Sxf+GD1A5aF6vdj2WwET+kIgBIKo1RUoFZUoETXllQssBQLS9MjLUOqCqoqSbgQonWQNbRFG/XII49gs9lYtWpVskNJeffeey933XUXF198MV26dGHfvn1s2rSJRx99NOF577//PsFgkNWrV7NlyxYWLlzIM888A0AgEGDJkiWsW7cOl8vFlClT2LBhAx6Ph88++4w33ngDv9/Piy++2BJvkYcffxyAt55/vkWuJ4QQDXHCfQJTQiGUYBAjMwMjMyOSOAOWE/B6MCCy9qxhoBhGPAFXQmGUcORcTBMFsBQlkowTWavWUlRQlUhSXmVbCCFanGFEkmypGBTihHb66afz+uuv88EHH3Dw4EHOPvtsJk6ciMfjSXjep59+yqBBgwDo27cvW7dujR+z2+2sWrUKl8sFQDgcxuFwsGnTJnr16sXEiRPx+Xzcd999zffGhBAixZ04iXa8FVsndEonrEQz8UZbgSxdj5TzHDWOKZaImxZYJophRrpoGkYkkQ8bKEYYpSIcScoVBYtYCzkouo7iD0QScUXBUjVJyoUQTcs0MWVpLyEE4PV6GTFiRKPO8fl81ZJxTdMIh8Pouo6qqrRr1w6AlStXUl5ezsCBA3n33XfZt28fy5cvZ8+ePdx55528++679XZTF0KItuiESLTVUBjVKq/Rin3MYol49KmVqKxlRZJtwwQrkpAb3nQMXUMJhyNJeTiEEjIhbKBgYSlK5FUtQAFQsTQ1mpRHu67LuEshRCKGAdHWJiGEaCyPx0NZWVn8uWma6FWGopimyaJFi9i9ezfLli1DURQyMzPp0aMHdrudHj164HA4KCgoICcnJ+G1NE2rs4yu65FjxcVgt9f6Gc4WjSsrK+tY3uox03W9xa/ZEBJX46VqbHogEPn5dzqTHYo4Bs2SaNc3gcbatWtZsWIFqqoycuRIxowZA8CIESPwer1AZPmJBQsWNEE0CpbNTujkelqxm0s0Ka4686/l9WCGQzXLRpNyTDPSWm4alY/BUCRZN8KooTAEKqJN5ESTcSVyvqJGJndTKpNzGVcuxIlHMYzk3POEEG1Cv3792LBhA1deeSVbtmyhV69e1Y7PmDEDu93O008/jRpNfvv3788rr7zCzTffzMGDB/H7/WRmZtZ7LcMw6pxBPja7vFZUFFmusJZEOxQOA1BYWNjYt3lcsrKyWvyaDSFxNV6qxpZls0V+N+pJtDt16tRCEYnGaJZEO9EEGgCPPfYY69evx+12M3z4cIYPH44z+gO0cuXKJovjgQceoNhfQMUpHXHaW8EHzlhLtaY1rLU81l29Whd2AyVsQDgcaTE3jMi4csskmpFXExlLHh1PbhjRZF2SciHaAlnaS7RVDzzwABkZta8oIprGsGHDyMvLY9SoUViWxfz581m3bh3l5eX06dOHNWvWMGDAAMaPHw/AuHHjGDZsGJs3b+a6667DsixmzJiB1gL3oemTJjX7NYQQorGaJdFONIEGQO/evSktLUXXdSzLQlEUtm3bht/v55ZbbiEcDjNlyhT69u17XHGcf/75HPYfxhf0HdfrpKzoLOgNTsqPbik3DJRwOJKUG5GkXQ0EZLI3IdoAC2TGcdFmnX/++bKOdjNTVZXZs2dX29ezZ8/49rZt22o9LxkToJ1/3nktfk0hhKhPs3wKSzSBBkRmwBw5ciQul4thw4aRnp6O0+nk1ltv5frrr+ebb77htttu49133602Huho9Y3pyc/PpyhQRO++vXHqqTO2IVXHgSi6TlrXLvHJ3uKJeXQ7NtkbsbHloVBlS7kVSfMre7JHu6srkYQ93kquKJESCtX3VS1TC03XyEzB1otUjSugGuiaTmY94+JE26VYlszlINqszZs3k5GRUaM7szgxbf78c0ASbiFEammWRDvRBBrbtm3jww8/5IMPPsDtdjN16lTeeecdLr/8crp27YqiKHTv3p3MzEwOHTqUcMxBfWN6pk2bRsgMsfSlpSmVaKfsOJCGxKVrka+YWiZ7i7WOK6YJ0RnZFSta1op2czdNsKzK/aYJpkW8XV4Bhchs7VgWGRkZlJSUABaKFZkszqqWmMfGq0cSeSua5Edeq2ZybyVK+BshMyODouLiRp3TEoK+EjKMcL2tPTKmp42KJdmSaIv/3969R0dd3/kff37nlgsZLgnQRTEoINRdTo0I63ExYAvRLXisJGAuu0RPqezaPd0tLoq0khMEgQj0Z8GVwrpeCLWEBU5r0pVaEIvLYVm0RNatMRZQy9qaAEHIfWa+398fkxlymdxgkplMXg/1kPne5pVkeDvv+Xy/n2+MWr9+ve6jLUFrn38e0H20RSS69Emj3dUEGm63m/j4eOLi4rDb7SQnJ3Pp0iX27NlDZWUlhYWFfPHFF9TW1jJq1Ki+iCfhFGqyt2s5XsvI+JWm3P+fd8QIPBdqWtZbbbZr08RDS8NvBZv54IcBFv6GvmUbwwxs1+q5As1+oPcONPttQ2JYBpYB2O3Y6utbfefB7v5Ks29cWRZ4bIVY1nbUX+Qa+HxYOm1cREREJGL65J1YVxNoZGdnk52dTV5eHk6nk9TUVObPnw/AihUryM3NxTAM1q5d2+Vp4xKjOms2nU4slzPkLtfU2Ic8oBWy4TesVutbmn1z2HA8F2s6jNgDV5r9Vg1/sNm3TP+pvVarZt+8sl+g2TdaRunbfo/+R8Fmv8M6w39avwxePh9WYmKkU4iIiIgMWn3SyXY3gUZubi65ubkd9tu0aVNfxBHpnU5OIQ/V0FuJCVhNjX3X7Ldr+IMNfSBRqA8BLAuPJ9HfaHnDHUwGBNMEZ+gPpkRERESk72nIWCQa9aLZD7Xc57WBywVeM+zRJPoZponlckU6hoiIiMigFdON9qpVq7jYdDHSMURE+pdhYOnWexLDVq1axfDhwyMdQ6LE6mXLIh1BRKSDmG60p0yZEtv30RYRCcWydA9tiWlTpkzRfbQlaMrkyZGOICLSQUy/Ezt8+DCXmi8xZfqUSEcRkRhimiaFhYV89NFHuFwu1qxZw7hx4zpst3LlSoYNG8ayltGWBx54ALfbDcDYsWNZt25d34XUrb0khh0+fJihQ4eSlpYW6SgSBQ4fOwbAzDvuiHASEZErYrrR/vGPfxy8j7aISLgcOHCA5uZmSkpKKC8vZ/369WzdurXNNrt27aKyspLp06cD0NTUBEBxcXHfhgvc2ku3iZMY9uMf/1j30Zag//fii4AabRGJLrqIT0Skl9577z3S09MBSEtL44MPPmiz/sSJE7z//vtkZ2cHl1VUVNDQ0MC3v/1t8vPzKS8v75twmghNREREJOLUaIuI9FJtbS1JSUnBx3a7Ha/Xfy+1qqoqnn/+eQoKCtrsEx8fz+LFi/m3f/s3Vq1axbJly4L7hJXX659xXkREREQiJqZPHRcR6QtJSUnU1dUFH5umiaNl8rH9+/dTU1PDkiVLqK6uprGxkfHjx3Pfffcxbtw4DMPgpptuYvjw4VRXVzNmzJgun8tut5OSkhJyncPh8K9raLjSYDudMGYMtFwLHinBbFEmWnNB9GaLxlxOpxPDMKIul4iISIAabRGRXpo6dSqHDh1i7ty5lJeXM2nSpOC6/Px88vPzAdi3bx+nT58mMzOT1157jcrKSgoLC/niiy+ora1l1KhR3T6Xz+frdGblwKzLtgsXwDTB6cSor8eXkADNzeH5Zq9StM4IHa25IHqzRWMuj8eD0+nsUa7uPswSERHpCzHdaBcVFVHTVBPpGCISYzIyMjhy5Ag5OTlYlsXatWspLS2lvr6+zXXZrS1YsIAVK1aQm5uLYRisXbs2OAoeTpZu7SWDQFFRke6jLUEbfvjDSEcQEekgpt+NTZw4UffRFpGws9lsPP30022WTZgwocN2mZmZwa9dLhebNm3q82wG6NZeEvMmTpwYlSPtEhkTb7wx0hFERDqI6Ub7zTff5FLzJaalT4t0FBGRvmdZYLOp0ZaY9+abb+J2u7nzzjsjHUWiwJu/+Q0A98yaFeEkIiJXxHSjvW3bNjymR422iAwOXi+W0xnpFCJ9btu2bTidTjXaAsDWnTsBNdoiEl1iutEWERlUTBMrMTHSKUQkBpimSWFhIR999BEul4s1a9Ywbty44PqysjJeffVV7HY7kyZNorCwEJvNxgMPPIC75a4HY8eOZd26dZH6FkREIkqNtohIjDB8PizdQ1tEwuDAgQM0NzdTUlJCeXk569evZ+vWrQA0Njby3HPPUVpaSkJCAo899hiHDh3irrvuAqC4uDiS0UVEooIt0gFERCRMLMt/H20RkWv03nvvkZ6eDkBaWhoffPBBcJ3L5WLXrl0kJCQA4PV6iYuLo6KigoaGBr797W+Tn59PeXl5RLKLiEQDjWiLiMQQy6bPT0Xk2tXW1pKUlBR8bLfb8Xq9OBwObDYbI0eOBPyj1/X19cyYMYPKykoWL17MwoUL+eSTT3jkkUfYv39/n9zKUEQk2sV05du8eTM1jbqPtogMInpDK4PA5s2bGTFiRKRjxLSkpCTq6uqCj03TbNMwm6bJhg0bOHPmDFu2bMEwDG666SbGjRsX/Hr48OFUV1czZsyYLp/LbreTkpIScp3D4fCvsyw4fx5cLoiLa7NN8ebNAJ2/JiwLGhvB5+u4LnCnhtZ/9pDD4YjK16Fy9V60ZnM0Nvpf//HxkY4iVyGm35Fdf/31xDXE6T7aIjJ46NZeMghcf/31uo92H5s6dSqHDh1i7ty5lJeXM2nSpDbrCwoKcLlcvPDCC9hamtM9e/ZQWVlJYWEhX3zxBbW1tYwaNarb5/L5fJ3+LoO/Z8MAtxvbuXMY1dX++ShaLpVJajmFvaam3eCK14vR1ASGgTl0KJbb7a+RPh/4fBimCc3N0NCA0dyM4fWCaQJgAUbLYaz2zbjhXzNixIiOzxkFlKv3ojXbCKfT//rvptHu7sMsiYw+abS7m6ny9ddf5+WXX8Zms5GVlUVeXl5w3fnz58nMzOSll15iwoQJ15TjF7/4BZebL3PXPXdd03FERKKez+e/tZdhdL+tyAD3i1/8ArfbzTe+8Y1IR4lZGRkZHDlyhJycHCzLYu3atZSWllJfX8+UKVPYs2cP06ZN46GHHgIgPz+fBQsWsGLFCnJzczEMg7Vr14b3tPG4OMzrr8eor8eorsaorcVKSODnBw4A8MC99/pHr5uawOsFlwtz9Gj/3RhafwjZkslqdejg16bZthH3+cDjAY/H34w3Nvrv8ADgdGLU1YFhtG3GbTbVYhHpm0a7q5kqAZ599lnKyspITExk3rx5zJs3j2HDhuHxeCgoKCA+TKdH7NixA4/pUaMtIjHPME3MdqdTisSqHTt24HQ61Wj3IZvNxtNPP91mWesBkIqKipD7bdq0qU9zAViJiVipqRiXL2M7f54dJSVYNhvzW2Y9N91urKFD/aeY97bhDTTKTmebRhxCNOPDh2MmJPib+uZmfzPu8fhH0S3LPypuGP7mv30zrrOPRGJenzTaXc1UCTB58mQuX76Mw+HAsix/EQKKiorIyclh+/btfRFLRCR2+XwdrlsUEYlZhoE1dCi+IUOwHA7w+TBHjfKPXvf1XBWBZjw+3v98rQSb8ZZR8eDIuNd7ZWS8k2bcChz3Kq4XF5Ho0yeVqKuZKgFuvvlmsrKySEhIICMjg6FDh7Jv3z6Sk5NJT09Xoy0i0lum6Z8kSERkMLHb/ddqO53+UexoYbcHR61DjoxbVsfT1Fufou7x+Jvz1s04qBkXGUD6pNHuaqbKiooK3n77bQ4ePEhiYiKPP/44b7zxBnv37sUwDI4ePcqHH37I8uXL2bp1a5eTaHQ3S6XT6cTyWYwYMYJ4R/TM1he1MxtGaS6I3mzRmivOE4fD4cCd4o50FOknBmDpVEQRkYHBMDptxoNfW1bHkfFQzThtJ28jPt5/Knu7ydtEpH/1SaPd1UyVbreb+Ph44uLisNvtJCcnc+nSJX76058Gt1m0aBGFhYXdzlTZ3SyVHo8Hr+mlpqYmqhrtqJ3ZMEpzQfRmi9ZcDd4GUhJSOH+p6xl5NUtl7LAsS9f8iYjEEsPwnwbf1eRtoZrxpCSspqY2k7cFrhPv9HpxNeMiYdcnjXZXM1VmZ2eTnZ1NXl4eTqeT1NRU5s+f3xcx2L59OxcaL/TJsUVEoordrntoy6Cxfft2kpOTIx1DosSgvuQwVDOekoJFFzOpt79evNVM6iEnbzMM/3+aTV2kV/rkXVl3M1Xm5uaSm5vb6f7FxcVhyZGSkoLVYOk+2iIS+xwOXasng0ZKSoruoy1BnV1GKC16M5N662Y8MJN6y/3Fjebm4H3GsSyslqY72JwHGIZ/Xevm3DDajqyLDAIxPfxRUlJCbXMts++fHekoIiJ9ytJEaDKIlJSUkJSUxLx58yIdRaJASUkJANnZ2RFOMoD1pBkHf6McmMit3Z8G+B8HGvWW5t0InNre+lT2dscONuuBRjzQrLdu1Ns/FolyMd1o7969G4/pCdloe0wPHtPjv66xHQsLg9B/gQPLrVZlJ7C9gRGcFTLwdWB7AwP/vwY+04dpmVfWqViIyDVSoy2Dye7du3E6nWq0BfC/HkCNdr9o3fC20/EddbvlKSn4hg0L3ay3fG0E1rVq1DHNK6PqHo9/Wav371ZLc95+ZN0KZGzdnAe+1hlg0g9iutFuz2N6aPY1Y1kWcY44RsaPJM4Rh81o+5ets+Y7sC7wj/9fK7i9aZmYmJim/0/LsoLLgOByn+XD4/P4t7XMDs/V/nkDTXzrHK31tMFvvU7NvUgMMQzdQ1tERAaGa2nWgwtCj6p3aNZbGvXWI+tGYF1rTifU1fnfH5tmcMTcat+kg5p16bFB0Wg3mU34PD5cdhejEkaR4EzAaXNGLE/K8BTcviu3XQo06oGmPfhPy9eBbUI1+IE/e9Lgm5hYPqvTBt/Z7KTOUxfMEo4Gv/W2oYRaHupDAMuyQn4Aog8MRPD/z14ToYmIyGARuD1aCFfVrI8YgVld3bZRDzTpLfczx+u90qwHRttbHcsyWg1kBd6ztr5evf2p75pcLubF/Dszm2FjTOKYiDfXXWndrHbSj/aJ9g1+8ohkzpnnetTgBxr1zhr89o1xoOlvr/1yywxdHhu8DTT6Gtvu283ZAN0JdYlAyGUt30eopr71hxOd7Q8dP1AI/HxDPle73bu6lKEzHtPTq+1lgLPbdQ9tERGR3mjdrLtc/vuP03mjHnJdd6PqrZv19qfBt55crtWx25wC73a3f0YZQGK+0bYbdobGDY10jKjUvsF32BxR+2FESnIK563uZ5cNNeoNoUfke7VtJ/unDE/hvO98t9tdy/OEXtTJMVs91xDXEC7XXw65ncQW0+32v0kQERGR/hM4fbzdh91X3ay3n1xu+HBobOx4EBkQYrrRDtdtwmTg6OxU8pAjwmE4e8Bld+GyR2eDE625pA+0fAovMlgUFxeTkpJCQ0NDpKNIFND7PRnQumrW3W7/bdZkQIrpRjsxMTHSEURERCTMEhMTSUxMVKMtgN7viUh0iulG+5VXXgHg4YcfjmgOERERCZ9XXnmFIUOGsHDhwkhHkSig93siEo1iel760tJSSktLIx1DREREwqi0tJS9e/dGOoZECb3fE5FoFNONtoiIiIiIiEh/U6MtIiIiIiIiEkZqtEVERERERETCSI22iIiIiLRhmiYFBQVkZ2ezaNEiPv300zbry8rKWLhwITk5ORQUFGCaZnDd+fPnmTVrFqdOnerv2CIiUSOmZx3XRCkiIiKxZ+/evaSkpHD+/PlIR4lZBw4coLm5mZKSEsrLy1m/fj1bt24FoLGxkeeee47S0lISEhJ47LHHOHToELNnz8bj8VBQUEB8fHy/ZdX7PRGJRhrRFhHppe5GegJWrlzJxo0be7WPiEg0eO+990hPTwcgLS2NDz74ILjO5XKxa9cuEhISAPB6vcTFxQFQVFRETk4Oo0eP7v/QIiJRJKZHtEVE+kJXIz0Bu3btorKykunTp/d4HxGRaFFbW0tSUlLwsd1ux+v14nA4sNlsjBw5EoDi4mLq6+uZMWMG+/btIzk5mfT0dLZv397j57Lb7aSkpIRc53A4Ol0XadGaTbl6L1qzRWsu6Rk12iIivdTVSA/AiRMneP/998nOzub06dM92kdEJJokJSVRV1cXfGyaJg6Ho83jDRs2cObMGbZs2YJhGOzduxfDMDh69Cgffvghy5cvZ+vWrYwaNarL5/L5fJ1eBhDNlwhEazbl6r1ozdbTXGPGjOmHNNJbOnVcRKSXOhvpAaiqquL555+noKCgx/uIiESbqVOncvjwYQDKy8uZNGlSm/UFBQU0NTXxwgsvBE8h/+lPf8rOnTspLi7mlltuoaioqNsmW0QkVmlEW0Skl7oa6dm/fz81NTUsWbKE6upqGhsbGT9+fLejQ53RKZXhFa25IHqzKdfglJGRwZEjR8jJycGyLNauXUtpaSn19fVMmTKFPXv2MG3aNB566CEA8vPzycjIiHBqEZHo0SeNtmmaFBYW8tFHH+FyuVizZg3jxo0Lrn/99dd5+eWXsdlsZGVlkZeXh8/n46mnnuLMmTPY7XbWrVtHampqX8QTEbkmU6dO5dChQ8ydO7fDSE9+fj75+fkA7Nu3j9OnT5OZmcmvfvWrTvfpik6pDK9ozQXRm22g59IplVfHZrPx9NNPt1k2YcKE4NcVFRVd7l9cXNwnuUREBoo+abS7m/Tn2WefpaysjMTERObNm8e8efM4fvw44J9A6NixY6xbt04TBYlIVOpqpCc7O7vH+4iIiIhIbOqTRru7SX8mT57M5cuXcTgcWJaFYRjMmTOHu+++G4DPP/88OJuliEi06W6kJyAzM7PLfUREREQkNvVJo93VLSEAbr75ZrKyskhISCAjI4OhQ4f6wzgcLF++nF//+tds3ry52+fRtYvhFa25IHqzRWsuiO5sIiIiIiKxzLAsywr3QdetW8ett97K3LlzAZg5c2Zw5sqKigq+//3v8+///u8kJiby+OOPk5GRwTe/+c3g/tXV1Tz44IP88pe/JDExMdzxRERERERERPpMn9zeq6tbQrjdbuLj44mLi8Nut5OcnMylS5f4+c9/zrZt2wBISEjAMAzsdntfxBMRERERERHpM30yoh2YdbyysjI46c/vfve74ERBP/vZz9i7dy9Op5PU1FRWr16N1+tlxYoVnDt3Dq/XyyOPPMKcOXPCHU1ERERERESkT/VJoy0iIiIiIiIyWPXJqeMiIiIiIiIig5UabREREREREZEwUqMtIiIiIiIiEkYDvtE2TZOCggKys7NZtGgRn376aZv1b731FllZWWRnZ7N79+6oyVVWVsbChQvJycmhoKAA0zSjJlvAypUr2bhxY9TkOnnyJHl5eeTm5vKP//iPNDU1RU22119/nfnz55OVlcVrr73Wb7kC3n//fRYtWtRheaRe/xJeqnPhzxagOtezXJGucaA6F+tU58KbK6C/a1xPsqnOhaYaF4OsAe5Xv/qVtXz5csuyLOvEiRPW3//93wfXNTc3W3PmzLEuXrxoNTU1WZmZmVZVVVXEczU0NFizZ8+26uvrLcuyrKVLl1oHDhzol1zdZQv42c9+Zj344IPWhg0boiKXaZrW/fffb33yySeWZVnW7t27rVOnTkVFNsuyrBkzZlg1NTVWU1NT8DXXX7Zv327dd9991sKFC9ssj+TrX8JLdS682QJU53qWy7IiW+MsS3VuMFCdC1+ugEjUuO6yqc6FphoXmwb8iPZ7771Heno6AGlpaXzwwQfBdadOnSI1NZVhw4bhcrm4/fbbeffddyOey+VysWvXLhISEgDwer3ExcX1S67usgGcOHGC999/n+zs7H7L1F2uM2fOMHz4cF599VX+9m//losXLzJ+/PioyAYwefJkLl++THNzM5ZlYRhGv2VLTU1ly5YtHZZH8vUv4aU6F95soDrXm1wQ2RoHqnODgepc+HJB5Gpcd9lU50JTjYtNA77Rrq2tJSkpKfjYbrfj9XqD69xud3DdkCFDqK2tjXgum83GyJEjASguLqa+vp4ZM2b0S67uslVVVfH8889TUFDQb3l6kqumpoYTJ06Ql5fHyy+/zH/9139x9OjRqMgGcPPNN5OVlcW8efO4++67GTp0aL9lu/fee3E4HB2WR/L1L+GlOhfebKpzvcsFka1xKBmx8wAACzZJREFUoDo3GKjOhS9XJGtcd9lU50JTjYtNA77RTkpKoq6uLvjYNM3gC7X9urq6ujYv1kjlCjwuKiriyJEjbNmypV8/Nesq2/79+6mpqWHJkiVs376dsrIy9u3bF/Fcw4cPZ9y4cUycOBGn00l6enqHTyIjla2iooK3336bgwcP8tZbb3HhwgXeeOONfsvWmUi+/iW8VOfCm011rne5orXGgepcLFGdC1+uSNa47rKpzvWOatzANuAb7alTp3L48GEAysvLmTRpUnDdhAkT+PTTT7l48SLNzc28++673HbbbRHPBVBQUEBTUxMvvPBC8JSj/tJVtvz8fPbt20dxcTFLlizhvvvuIzMzM+K5brjhBurq6oITV7z77rvcfPPN/ZKru2xut5v4+Hji4uKw2+0kJydz6dKlfsvWmUi+/iW8VOfCm011rne5orXGgepcLFGdC1+uSNa47rKpzvWOatzA1vEchQEmIyODI0eOkJOTg2VZrF27ltLSUurr68nOzubJJ59k8eLFWJZFVlYWX/nKVyKea8qUKezZs4dp06bx0EMPAf6imJGREfFskbiWp6e5nnnmGf75n/8Zy7K47bbbuPvuu6MmW3Z2Nnl5eTidTlJTU5k/f36/ZWsvGl7/El6qc+HNpjrX+1zRVONAdS4Wqc6FL1cka1xPsqnOdS8aXvty7QzLsqxIhxARERERERGJFQP+1HERERERERGRaKJGW0RERERERCSM1GiLiIiIiIiIhJEabREREREREZEwUqMtIiIiIiIiEkZqtHvp448/ZsmSJSxatIisrCw2b96MZVkcO3aMpUuXRjoe27dv5+TJk52uX7RoEadOneqwfOfOnR2W/eEPf+Bb3/oWy5cv73WOX//613zxxRecPXuWBx98sNf7X63Dhw9TUlLSo22rq6spLCwE4Pjx41RUVFzVc0bL714kXFTnekZ1TmTgUp3rGdU5kaunRrsXLl26xGOPPcYPfvADiouL2b17N5WVlezatSvS0YKWLFnC1772tV7vt3Xr1g7Lfvvb33LnnXdSVFTU6+Pt2LGD2traXu93rWbOnNnj+0eOGjUqWJj37t1LVVVVHyYTGRhU53pOdU5kYFKd6znVOZGr54h0gIHk4MGD3HHHHdx4440A2O12ioqKcDqdnDhxIrjdzp07efPNN/F6vbjdbrZs2cL//d//sWLFChwOB3a7nWeffRan08n3v/99LMvC4/GwatUqJk+eHDzO/PnzefHFFxk6dCh33HEHO3fu5M///M+ZP38+JSUllJSUUFZWhmEYzJ07l/z8fJ588knmzp3LX/7lX/LEE09QVVXFmDFjOH78OP/5n/8JwL/8y79w7tw5Ghoa+NGPfkRZWRlffvklhYWFwUL1+eefs3XrVhobG0lNTSUtLY3Vq1djt9uJi4tj9erVmKbJo48+yvDhw5k5cyaPPPIIAG+//TYffvghy5cvZ8OGDVy4cIHvfve7VFdXM3nyZNasWcMf//hHVq5cSVNTU/B4Y8aMCX7v+/bt49ChQzQ2NlJdXU1+fj4HDx7k448/5oknnmDOnDkhf85lZWWcPn2aZcuW8dJLL/HLX/4Sh8PBtGnTePzxx9myZQsnTpygvr6eZ555hhUrVlBQUMA777zD//7v/3LhwgXeeustNm/eDEBOTg6bN29m9OjRAJimyZo1azh58iQej4fvfe97uN3usP/uRSJFdU51TnVOYp3qnOqc6pz0BzXavVBVVcUNN9zQZtmQIUPaPDZNk4sXL/LKK69gs9lYvHgx//M//0NFRQV/8Rd/wZNPPsm7777Ll19+yeeff47b7WbTpk38/ve/7/CJ4ezZs3nnnXf4sz/7M8aOHcuRI0dwuVzceOONfPbZZ/zHf/wHr732GoZh8PDDD3PXXXcF9y0pKWHs2LFs3ryZU6dOcd999wXXzZo1i29961ts2bKF/fv38+ijj7Jz585gUQa47rrrWLJkCadPnyYvL4/MzEyeeeYZbrnlFg4cOMD69et54oknqK6uZu/evbhcruC+d999N7fccguFhYU4nU5qa2tZt24dbrebjIwMzp8/T1FREYsWLWLWrFkcPXqUjRs3smnTpjbff11dXbC4vvLKK+zevZtjx46xY8cOvvGNb4T8OQd89NFHvPHGG+zatQuHw8H3vvc9Dh06BMD48eN56qmnOHv2LABTpkwhPT2duXPnkp6ezk9+8hO+/PJLqqurGTFiRLAog/9/zjU1NezZs4fq6mp27tzJX/3VX4X9dy8SKapzqnOqcxLrVOdU51TnpD+o0e6F6667jt/97ndtlv3hD3/gT3/6U/CxzWbD6XTy2GOPkZiYyJ/+9Ce8Xi8LFizgX//1X/nOd76D2+1m6dKlzJw5k08++YTvfve7OBwOHn300TbHvueee/jJT37CmDFjWLp0KcXFxViWxT333ENlZSWff/45Dz/8MABffvkln332WXDfU6dOMXPmTAAmTJhAcnJycN2UKVMAGDlyJOfOnevR915VVcUtt9wCwPTp04NFdOzYsW2Kcig33HADw4YNAyAlJYWGhgYqKyvZtm0bL774IpZl4XQ6O+wXeD63282ECRMwDINhw4bR1NTU6c854PTp09x6663B406bNo2PP/4YgJtuuqnTrIZhcP/991NWVsbZs2dZsGBBm/VnzpwhLS0N8J+qtHTpUo4dOwaE93cvEimqc6pzqnMS61TnVOdU56Q/6BrtXvj617/OO++8EyyAHo+H9evXU1lZGdymoqKCAwcO8Nxzz7Fy5UpM08SyLA4ePMjtt9/Oq6++yl//9V/z4osvcuzYMUaPHs1LL73Eo48+yo9+9KM2zzdp0iTOnj3LyZMnmTVrFvX19Rw8eJCZM2cyfvx4Jk6cyI4dOyguLiYzM5NJkya12Tdw+tNnn31GTU1Nl9+bZVldrh89enRwconjx48HT7ey2UK/hAzDCB7TMIwO68ePH8+yZcsoLi5m1apV3HvvvSGP0ZnOfs6tj3/y5Em8Xi+WZXH8+PFgQQ6VuXXerKws9u/fz/Hjx5k1a1aH3IFPWi9fvszixYu7zXQ1v3uRSFGdU51TnZNYpzqnOqc6J/1BI9q9kJSUxPr163nqqaewLIu6ujq+/vWvk5eXx3//938DMG7cOBISEsjMzMTlcjFq1CiqqqpIS0sLXlNis9lYsWIF1113HUuXLuXVV1/FZrPxD//wDx2ec/r06Zw9exabzcb06dP5/e9/z5AhQ/jqV7/KnXfeSW5uLs3NzXzta1/jK1/5SnC/BQsW8OSTT/I3f/M3XHfddcTFxXX5vU2YMIFly5axcePGkOvXrFnD6tWrsSwLu93O2rVruzzebbfdxhNPPMHq1atDrl++fDmFhYU0NTXR2NjID3/4wy6P115nP+eAyZMn881vfpPc3FxM0+T2229nzpw5nc5Eeeutt7Jx40bGjh3LhAkTGDJkCGlpaTgcbf+KzJ49m6NHj5Kbm4vP52vzOwv3714kElTnVOdU5yTWqc6pzqnOSX8wrO4++pIB6be//S319fXcddddfPLJJ3znO9/hwIEDkY7V53bv3s0f//hH/umf/umajvN3f/d3/OAHP2DcuHFhSiYi4aY6pzonEutU51TnZODSqeMx6oYbbmDbtm3k5OSwbNkyCgoKIh2pz/3mN79hx44dzJgx46qP0djYSGZmJl/96ldVlEWinOrc1VGdExk4VOeujuqcRAONaIuIiIiIiIiEkUa0RURERERERMJIjbaIiIiIiIhIGKnRFhEREREREQkjNdoiIiIiIiIiYaRGW0RERERERCSM1GiLiIiIiIiIhNH/B0PiTLDUmIfgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_performances_plots(performances_df_lr, \n",
    "                       performance_metrics_list=['AUC ROC', 'Average precision', 'Card Precision@100'], \n",
    "                       expe_type_list=['Test','Validation'], expe_type_color_list=['#008000','#FF0000'],\n",
    "                       parameter_name=\"Class weight for the majority class\",\n",
    "                       summary_performances=summary_performances_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to decision trees, the results are mitigated. Slightly higher performances are obtained for AUC ROC with a low class weight for the majority class. The performances in terms Average Precision and CP@100 however follow the opposite trend. \n",
    "\n",
    "## Summary\n",
    "\n",
    "The benefits of relying on misclassification costs in the training procedure therefore appear to be strongly dependent on the characteristics of a dataset and on the performance metric to optimize. The experiments provided in this section showed that cost-sensitive learning effectively allows to shift the decision boundary of a classifier and to favor the classification of the minority class. Its benefits in terms of AUC ROC and Average Precision seem however conflicting. In particular, the shift of the decision boundary seems to lead to many false positives, that negatively impact the precision, and therefore, the performance in terms of Average Precision.   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving of results\n",
    "\n",
    "Let us finally save the performance results and execution times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "performances_df_dictionary = {\n",
    "    \"Decision Tree\": performances_df_dt,\n",
    "    \"Logistic Regression\": performances_df_lr\n",
    "}\n",
    "\n",
    "execution_times = [execution_time_dt,\n",
    "                   execution_time_lr\n",
    "                  ]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both data structures are saved as a Pickle file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filehandler = open('performances_cost_sensitive.pkl', 'wb') \n",
    "pickle.dump((performances_df_dictionary, execution_times), filehandler)\n",
    "filehandler.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
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}
